refactor

example/09_convnd_fwd/convnd_fwd_xdl_fp16.cpp

@@ -4,7 +4,7 @@
 #include "convnd_fwd_common.hpp"
 
 #include "ck/tensor_operation/gpu/device/device_convnd_fwd_nwc_kxc_nwk_xdl.hpp"
-#include "ck/tensor_operation/gpu/device/device_convnd_fwd_multiple_d_nwc_kxc_nwk_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/device/device_conv_fwd_multiple_d_xdl_cshuffle.hpp"
 
 using InDataType  = ck::half_t;
 using WeiDataType = ck::half_t;
@@ -67,48 +67,51 @@ static constexpr auto ConvSpec =
 static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 template <ck::index_t NDimSpatial>
-using DeviceConvNDFwdInstance =
-    ck::tensor_operation::device::DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle<
-        NDimSpatial,      //
-        InDataType,       //
-        WeiDataType,      //
-        AccDataType,      //
-        CShuffleDataType, //
-        ck::Tuple<>,      //
-        OutDataType,      //
-        InElementOp,      // Input Elementwise Operation
-        WeiElementOp,     // Weights Elementwise Operation
-        OutElementOp,     // Output Elementwise Operation
-        ConvSpec,         // ConvForwardSpecialization
-        GemmSpec,         // GemmSpecialization
-        1,                //
-        256,              // BlockSize
-        128,              // MPerBlock
-        256,              // NPerBlock
-        32,               // KPerBlock
-        8,                // K1
-        32,               // MPerXdl
-        32,               // NPerXdl
-        2,                // MXdlPerWave
-        4,                // 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
-        1,                // ABlockLdsExtraM
-        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
-        1,                // BBlockLdsExtraN
-        1,
-        1,
-        S<1, 32, 1, 8>,
-        8>;
+using DeviceConvNDFwdInstance = ck::tensor_operation::device::DeviceConvFwdMultipleD_Xdl_CShuffle<
+    NDimSpatial,
+    ck::tensor_layout::convolution::NWC,
+    ck::tensor_layout::convolution::KXC,
+    ck::tensor_layout::convolution::NWK,
+    ck::Tuple<>,
+    InDataType,
+    WeiDataType,
+    AccDataType,
+    CShuffleDataType,
+    ck::Tuple<>,
+    OutDataType,
+    InElementOp,
+    WeiElementOp,
+    OutElementOp,
+    ConvSpec,    // ConvForwardSpecialization
+    GemmSpec,    // GemmSpecialization
+    1,           //
+    256,         // BlockSize
+    128,         // MPerBlock
+    256,         // NPerBlock
+    32,          // KPerBlock
+    8,           // K1
+    32,          // MPerXdl
+    32,          // NPerXdl
+    2,           // MXdlPerWave
+    4,           // 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
+    1,           // ABlockLdsExtraM
+    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
+    1,           // BBlockLdsExtraN
+    1,
+    1,
+    S<1, 32, 1, 8>,
+    8>;
 #endif
 
 int main(int argc, char* argv[])

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

@@ -23,7 +23,8 @@ namespace device {
 template <ck::index_t NDimSpatial,
           typename ALayout,
           typename BLayout,
-          typename DELayout,
+          typename DsLayout,
+          typename ELayout,
           typename ADataType,
           typename BDataType,
           typename DsDataType,

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <functional>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <sstream>
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_conv_fwd_multiple_d.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
+#include "ck/device_utility/device_prop.hpp"
+#include "ck/device_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+namespace {
+
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatDsPointer,
+          typename FloatE,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename Block2ETileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_gemm_multiple_d_xdl_cshuffle(const FloatAB* __restrict__ p_a_grid,
+                                            const FloatAB* __restrict__ p_b_grid,
+                                            FloatDsPointer p_ds_grid,
+                                            FloatE* __restrict__ p_e_grid,
+                                            const AElementwiseOperation a_element_op,
+                                            const BElementwiseOperation b_element_op,
+                                            const CDEElementwiseOperation cde_element_op,
+                                            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
+                                            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
+                                            const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                                                ds_grid_desc_mblock_mperblock_nblock_nperblock,
+                                            const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                                                e_grid_desc_mblock_mperblock_nblock_nperblock,
+                                            const Block2ETileMap block_2_etile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
+                                                  p_b_grid,
+                                                  p_ds_grid,
+                                                  p_e_grid,
+                                                  p_shared,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  cde_element_op,
+                                                  a_grid_desc_ak0_m_ak1,
+                                                  b_grid_desc_bk0_n_bk1,
+                                                  ds_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  e_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  block_2_etile_map);
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_ds_grid;
+    ignore = p_e_grid;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = cde_element_op;
+    ignore = a_grid_desc_ak0_m_ak1;
+    ignore = b_grid_desc_bk0_n_bk1;
+    ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = block_2_etile_map;
+#endif
+}
+} // namespace
+
+//
+// @brief      Device Convolution operation.
+//
+// Supports:
+//  @li         Forward convolution with up to 3 spatial dimentions
+//  @li         Input tensor in NWC data format
+//  @li         Weight tensor in KXC data format
+//  @li         Output tensor in NWK data format
+//
+// 1D:
+// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
+// 2D:
+// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
+// 3D:
+// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
+//
+template <index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename CShuffleDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          ConvolutionForwardSpecialization ConvForwardSpecialization,
+          GemmSpecialization GemmSpec,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t K1,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          index_t ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          index_t BBlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CDEBlockTransferScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched = make_default_loop_scheduler()>
+struct DeviceConvFwdMultipleD_Xdl_CShuffle : public DeviceConvFwdMultipleD<NDimSpatial,
+                                                                           ALayout,
+                                                                           BLayout,
+                                                                           DsLayout,
+                                                                           ELayout,
+                                                                           ADataType,
+                                                                           BDataType,
+                                                                           DsDataType,
+                                                                           EDataType,
+                                                                           AElementwiseOperation,
+                                                                           BElementwiseOperation,
+                                                                           CDEElementwiseOperation>
+{
+    using DeviceOp = DeviceConvFwdMultipleD_Xdl_CShuffle;
+
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+
+    static constexpr auto K1Number     = Number<K1>{};
+    static constexpr auto GemmK1Number = K1Number;
+
+    static constexpr auto matrix_padder =
+        MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
+
+    static auto GetWeightTensorDescriptor(index_t GemmNRaw, index_t GemmKRaw)
+    {
+        const auto wei_k_yxc_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(GemmNRaw, GemmKRaw));
+
+        const auto wei_gemmn_gemmk_grid_desc =
+            matrix_padder.PadBDescriptor_N_K(wei_k_yxc_grid_desc);
+
+        return wei_gemmn_gemmk_grid_desc;
+    }
+
+    static auto GetOutputTensorDescriptor(index_t GemmMRaw, index_t GemmN)
+    {
+        const index_t GemmM = math::integer_least_multiple(GemmMRaw, MPerBlock);
+
+        const auto out_gemmmraw_gemmn_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(GemmM, GemmN));
+
+        const auto out_gemmm_gemmn_grid_desc =
+            matrix_padder.PadCDescriptor_M_N(out_gemmmraw_gemmn_grid_desc);
+
+        return out_gemmm_gemmn_grid_desc;
+    }
+
+    template <index_t NDim, typename std::enable_if<NDim == 1, bool>::type = false>
+    static auto GetInputTensorDescriptor(index_t N,
+                                         index_t C,
+                                         index_t GemmMRaw,
+                                         index_t GemmKRaw,
+                                         const std::vector<index_t>& input_spatial_lengths,
+                                         const std::vector<index_t>& filter_spatial_lengths,
+                                         const std::vector<index_t>& output_spatial_lengths,
+                                         const std::vector<index_t>& conv_filter_strides,
+                                         const std::vector<index_t>& conv_filter_dilations,
+                                         const std::vector<index_t>& input_left_pads,
+                                         const std::vector<index_t>& input_right_pads)
+    {
+        const index_t Wi          = input_spatial_lengths[0];
+        const index_t Wo          = output_spatial_lengths[0];
+        const index_t ConvStrideW = conv_filter_strides[0];
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            const auto in_gemmmraw_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(GemmMRaw, GemmKRaw));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            const auto in_n_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Wi, C));
+
+            const auto in_n_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto in_gemmmraw_gemmkraw_grid_desc = transform_tensor_descriptor(
+                in_n_wo_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(N, Wo)), make_pass_through_transform(C)),
+                make_tuple(Sequence<0, 1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+        else
+        {
+            const index_t X             = filter_spatial_lengths[0];
+            const index_t ConvDilationW = conv_filter_dilations[0];
+            const index_t InLeftPadW    = input_left_pads[0];
+            const index_t InRightPadW   = input_right_pads[0];
+
+            const auto in_n_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Wi, C));
+
+            const auto in_n_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto in_n_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
+
+            const auto in_gemmmraw_gemmk_grid_desc =
+                transform_tensor_descriptor(in_n_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(N, Wo)),
+                                                       make_merge_transform(make_tuple(X, C))),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1, 3>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+    }
+
+    template <index_t NDim, typename std::enable_if<NDim == 2, bool>::type = false>
+    static auto GetInputTensorDescriptor(index_t N,
+                                         index_t C,
+                                         index_t GemmMRaw,
+                                         index_t GemmKRaw,
+                                         const std::vector<index_t>& input_spatial_lengths,
+                                         const std::vector<index_t>& filter_spatial_lengths,
+                                         const std::vector<index_t>& output_spatial_lengths,
+                                         const std::vector<index_t>& conv_filter_strides,
+                                         const std::vector<index_t>& conv_filter_dilations,
+                                         const std::vector<index_t>& input_left_pads,
+                                         const std::vector<index_t>& input_right_pads)
+    {
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            const auto in_gemmmraw_gemmkraw_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(GemmMRaw, GemmKRaw));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_ho_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
+                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmmraw_gemmk_grid_desc =
+                transform_tensor_descriptor(in_n_ho_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(N, Ho, Wo)),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+        else
+        {
+            const index_t Y = filter_spatial_lengths[0];
+            const index_t X = filter_spatial_lengths[1];
+
+            const index_t ConvDilationH = conv_filter_dilations[0];
+            const index_t ConvDilationW = conv_filter_dilations[1];
+
+            const index_t InLeftPadH = input_left_pads[0];
+            const index_t InLeftPadW = input_left_pads[1];
+
+            const index_t InRightPadH = input_right_pads[0];
+            const index_t InRightPadW = input_right_pads[1];
+
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_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(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), 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_gemmmraw_gemmk_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(N, Ho, Wo)),
+                                                       make_merge_transform(make_tuple(Y, X, C))),
+                                            make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmk_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+    }
+
+    template <index_t NDim, typename std::enable_if<NDim == 3, bool>::type = false>
+    static auto GetInputTensorDescriptor(index_t N,
+                                         index_t C,
+                                         index_t GemmMRaw,
+                                         index_t GemmKRaw,
+                                         const std::vector<index_t>& input_spatial_lengths,
+                                         const std::vector<index_t>& filter_spatial_lengths,
+                                         const std::vector<index_t>& output_spatial_lengths,
+                                         const std::vector<index_t>& conv_filter_strides,
+                                         const std::vector<index_t>& conv_filter_dilations,
+                                         const std::vector<index_t>& input_left_pads,
+                                         const std::vector<index_t>& input_right_pads)
+    {
+        const index_t Di = input_spatial_lengths[0];
+        const index_t Hi = input_spatial_lengths[1];
+        const index_t Wi = input_spatial_lengths[2];
+
+        const index_t Do = output_spatial_lengths[0];
+        const index_t Ho = output_spatial_lengths[1];
+        const index_t Wo = output_spatial_lengths[2];
+
+        const index_t ConvStrideD = conv_filter_strides[0];
+        const index_t ConvStrideH = conv_filter_strides[1];
+        const index_t ConvStrideW = conv_filter_strides[2];
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            const auto in_gemmmraw_gemmkraw_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(GemmMRaw, GemmKRaw));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            const auto in_n_di_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));
+
+            const auto in_n_do_ho_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_di_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(Do), make_tuple(ConvStrideD)),
+                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
+                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
+
+            const auto in_gemmmraw_gemmkraw_grid_desc = transform_tensor_descriptor(
+                in_n_do_ho_wo_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(N, Do, Ho, Wo)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0, 1, 2, 3>{}, Sequence<4>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+        else
+        {
+            const index_t Z = filter_spatial_lengths[0];
+            const index_t Y = filter_spatial_lengths[1];
+            const index_t X = filter_spatial_lengths[2];
+
+            const index_t ConvDilationD = conv_filter_dilations[0];
+            const index_t ConvDilationH = conv_filter_dilations[1];
+            const index_t ConvDilationW = conv_filter_dilations[2];
+
+            const index_t InLeftPadD = input_left_pads[0];
+            const index_t InLeftPadH = input_left_pads[1];
+            const index_t InLeftPadW = input_left_pads[2];
+
+            const index_t InRightPadD = input_right_pads[0];
+            const index_t InRightPadH = input_right_pads[1];
+            const index_t InRightPadW = input_right_pads[2];
+
+            const auto in_n_di_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_di_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Di, InLeftPadD, InRightPadD),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
+
+            const auto in_n_z_do_y_ho_x_wo_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(Z, Do), make_tuple(ConvDilationD, ConvStrideD)),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<1, 2>{},
+                           Sequence<3, 4>{},
+                           Sequence<5, 6>{},
+                           Sequence<7>{}));
+
+            const auto in_gemmmraw_gemmkraw_grid_desc = transform_tensor_descriptor(
+                in_n_z_do_y_ho_x_wo_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(N, Do, Ho, Wo)),
+                           make_merge_transform(make_tuple(Z, Y, X, C))),
+                make_tuple(Sequence<0, 2, 4, 6>{}, Sequence<1, 3, 5, 7>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmm_gemmk_grid_desc =
+                matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_grid_desc);
+
+            return in_gemmm_gemmk_grid_desc;
+        }
+    }
+
+    static index_t GetGemmMRaw(index_t N, const std::vector<index_t>& output_spatial_lengths)
+    {
+        return N * std::accumulate(std::begin(output_spatial_lengths),
+                                   std::end(output_spatial_lengths),
+                                   1,
+                                   std::multiplies<index_t>());
+    }
+
+    static index_t GetGemmKRaw(index_t C, const std::vector<index_t>& filter_spatial_lengths)
+    {
+        return C * std::accumulate(std::begin(filter_spatial_lengths),
+                                   std::end(filter_spatial_lengths),
+                                   1,
+                                   std::multiplies<index_t>());
+    }
+
+    static auto
+    MakeABEGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(index_t N,
+                                                    index_t K,
+                                                    index_t C,
+                                                    std::vector<index_t> input_spatial_lengths,
+                                                    std::vector<index_t> filter_spatial_lengths,
+                                                    std::vector<index_t> output_spatial_lengths,
+                                                    std::vector<index_t> conv_filter_strides,
+                                                    std::vector<index_t> conv_filter_dilations,
+                                                    std::vector<index_t> input_left_pads,
+                                                    std::vector<index_t> input_right_pads)
+    {
+        using namespace ck;
+
+        const index_t GemmMRaw = GetGemmMRaw(N, output_spatial_lengths);
+        const index_t GemmNRaw = K;
+        const index_t GemmKRaw = GetGemmKRaw(C, filter_spatial_lengths);
+
+        // A:
+        const auto in_gemmm_gemmk_grid_desc =
+            GetInputTensorDescriptor<NDimSpatial>(N,
+                                                  C,
+                                                  GemmMRaw,
+                                                  GemmKRaw,
+                                                  input_spatial_lengths,
+                                                  filter_spatial_lengths,
+                                                  output_spatial_lengths,
+                                                  conv_filter_strides,
+                                                  conv_filter_dilations,
+                                                  input_left_pads,
+                                                  input_right_pads);
+
+        // B:
+        const auto wei_gemmn_gemmk_grid_desc = GetWeightTensorDescriptor(GemmNRaw, GemmKRaw);
+
+        // E:
+        const auto out_gemmm_gemmn_grid_desc = GetOutputTensorDescriptor(GemmMRaw, GemmNRaw);
+
+        return make_tuple(
+            in_gemmm_gemmk_grid_desc, wei_gemmn_gemmk_grid_desc, out_gemmm_gemmn_grid_desc);
+    }
+
+    template <index_t NDim, typename std::enable_if<NDim == 1, bool>::type = false>
+    static auto GetABEGridDesc()
+    {
+        return MakeABEGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+            1, 1, 1, {1}, {1}, {1}, {1}, {1}, {1}, {1});
+    }
+
+    template <index_t NDim, typename std::enable_if<NDim == 2, bool>::type = false>
+    static auto GetABEGridDesc()
+    {
+        return MakeABEGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+            1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1});
+    }
+
+    template <index_t NDim, typename std::enable_if<NDim == 3, bool>::type = false>
+    static auto GetABEGridDesc()
+    {
+        return MakeABEGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+            1, 1, 1, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1});
+    }
+
+    using ABEGridDescs = decltype(GetABEGridDesc<NDimSpatial>());
+
+    using AGridDesc_M_K = remove_cvref_t<decltype(ABEGridDescs{}[I0])>;
+    using BGridDesc_N_K = remove_cvref_t<decltype(ABEGridDescs{}[I1])>;
+    using EGridDesc_M_N = remove_cvref_t<decltype(ABEGridDescs{}[I2])>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemmMultipleD_k0mk1_k0nk1_mn_xdl_cshuffle<
+        ADataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CShuffleDataType,
+        DsDataType,
+        EDataType,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CDEElementwiseOperation,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_M_K,
+        BGridDesc_N_K,
+        EGridDesc_M_N,
+        NumGemmKPrefetchStage,
+        BlockSize,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        K1,
+        K1,
+        MPerXDL,
+        NPerXDL,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_AK0_M_AK1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_AK1,
+        false,
+        ABlockLdsExtraM,
+        BBlockTransferThreadClusterLengths_BK0_N_BK1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_BK1,
+        false,
+        BBlockLdsExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CDEBlockTransferScalarPerVector_NPerBlock,
+        LoopSched>;
+
+    using AGridDesc_AK0_M_AK1 = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(AGridDesc_M_K{}))>;
+    using BGridDesc_BK0_N_BK1 = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(BGridDesc_N_K{}))>;
+
+    using Block2ETileMap = typename GridwiseGemm::DefaultBlock2ETileMap;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const ADataType* p_in_grid,
+                 const BDataType* p_wei_grid,
+                 EDataType* p_out_grid,
+                 index_t N,
+                 index_t K,
+                 index_t C,
+                 std::vector<index_t> input_spatial_lengths,
+                 std::vector<index_t> filter_spatial_lengths,
+                 std::vector<index_t> output_spatial_lengths,
+                 std::vector<index_t> conv_filter_strides,
+                 std::vector<index_t> conv_filter_dilations,
+                 std::vector<index_t> input_left_pads,
+                 std::vector<index_t> input_right_pads,
+                 AElementwiseOperation in_element_op,
+                 BElementwiseOperation wei_element_op,
+                 CDEElementwiseOperation out_element_op)
+            : p_a_grid_{static_cast<const ADataType*>(p_in_grid)},
+              p_b_grid_{static_cast<const BDataType*>(p_wei_grid)},
+              p_ds_grid_{}, // FIXME
+              p_e_grid_{static_cast<EDataType*>(p_out_grid)},
+              a_grid_desc_ak0_m_ak1_{},
+              b_grid_desc_bk0_n_bk1_{},
+              e_grid_desc_m_n_{},
+              e_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              block_2_etile_map_{},
+              a_element_op_{in_element_op},
+              b_element_op_{wei_element_op},
+              cde_element_op_{out_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads}
+        {
+            const auto descs =
+                DeviceOp::MakeABEGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
+                                                                          K,
+                                                                          C,
+                                                                          input_spatial_lengths,
+                                                                          filter_spatial_lengths,
+                                                                          output_spatial_lengths,
+                                                                          conv_filter_strides,
+                                                                          conv_filter_dilations,
+                                                                          input_left_pads,
+                                                                          input_right_pads);
+
+            const auto a_grid_desc_m_k = descs[I0];
+            const auto b_grid_desc_n_k = descs[I1];
+            e_grid_desc_m_n_           = descs[I2];
+
+            a_grid_desc_ak0_m_ak1_ =
+                GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k);
+            b_grid_desc_bk0_n_bk1_ =
+                GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k);
+
+            block_2_etile_map_ = Block2ETileMap{e_grid_desc_m_n_};
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
+                                           b_grid_desc_bk0_n_bk1_,
+                                           e_grid_desc_m_n_,
+                                           block_2_etile_map_))
+            {
+                e_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        e_grid_desc_m_n_);
+            }
+        }
+
+        //  private:
+        // pointers
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        typename GridwiseGemm::DsGridPointer p_ds_grid_;
+        EDataType* p_e_grid_;
+
+        // tensor descriptors
+        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
+        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
+        StaticallyIndexedArray<
+            typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+            NumDTensor>
+            ds_grid_desc_mblock_mperblock_nblock_nperblock_; // FIXME: Ds desc may be of different
+                                                             // type from E
+        EGridDesc_M_N e_grid_desc_m_n_;
+        typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+            e_grid_desc_mblock_mperblock_nblock_nperblock_;
+
+        // block-to-e-tile map
+        Block2ETileMap block_2_etile_map_;
+
+        // element-wise op
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CDEElementwiseOperation cde_element_op_;
+
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+        std::vector<index_t> filter_spatial_lengths_;
+        std::vector<index_t> conv_filter_strides_;
+        std::vector<index_t> input_left_pads_;
+        std::vector<index_t> input_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+#if 1
+            {
+                std::cout << "arg.a_grid_desc_ak0_m_ak1_{"
+                          << arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) << ", "
+                          << arg.a_grid_desc_ak0_m_ak1_.GetLength(I1) << ", "
+                          << arg.a_grid_desc_ak0_m_ak1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.b_grid_desc_bk0_n_bk1_{"
+                          << arg.b_grid_desc_bk0_n_bk1_.GetLength(I0) << ", "
+                          << arg.b_grid_desc_bk0_n_bk1_.GetLength(I1) << ", "
+                          << arg.b_grid_desc_bk0_n_bk1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.e_grid_desc_m_n_{ " << arg.e_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.e_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+            }
+#endif
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
+                                            arg.b_grid_desc_bk0_n_bk1_,
+                                            arg.e_grid_desc_m_n_,
+                                            arg.block_2_etile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemmMultipleD_k0mk1_k0nk1_mn_xdl_cshuffle has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
+
+            const auto K =
+                arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
+
+            auto launch_kernel = [&](auto has_main_k_block_loop) {
+                constexpr bool has_main_loop = has_main_k_block_loop.value;
+
+                const auto kernel = kernel_gemm_multiple_d_xdl_cshuffle<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    typename GridwiseGemm::DsGridPointer,
+                    EDataType,
+                    AElementwiseOperation,
+                    BElementwiseOperation,
+                    CDEElementwiseOperation,
+                    DeviceOp::AGridDesc_AK0_M_AK1,
+                    DeviceOp::BGridDesc_BK0_N_BK1,
+                    ck::StaticallyIndexedArray<
+                        typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+                        NumDTensor>,
+                    typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+                    Block2ETileMap,
+                    has_main_loop>;
+
+                return launch_and_time_kernel(stream_config,
+                                              kernel,
+                                              dim3(grid_size),
+                                              dim3(BlockSize),
+                                              0,
+                                              arg.p_a_grid_,
+                                              arg.p_b_grid_,
+                                              arg.p_ds_grid_,
+                                              arg.p_e_grid_,
+                                              arg.a_element_op_,
+                                              arg.b_element_op_,
+                                              arg.cde_element_op_,
+                                              arg.a_grid_desc_ak0_m_ak1_,
+                                              arg.b_grid_desc_bk0_n_bk1_,
+                                              arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                              arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                              arg.block_2_etile_map_);
+            };
+
+            float avg_time = 0;
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                avg_time = launch_kernel(integral_constant<bool, true>{});
+            }
+            else
+            {
+                avg_time = launch_kernel(integral_constant<bool, false>{});
+            }
+
+            return avg_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+#if 1
+        {
+            std::cout << "arg.a_grid_desc_ak0_m_ak1_{" << arg.a_grid_desc_ak0_m_ak1_.GetLength(I0)
+                      << ", " << arg.a_grid_desc_ak0_m_ak1_.GetLength(I1) << ", "
+                      << arg.a_grid_desc_ak0_m_ak1_.GetLength(I2) << "}" << std::endl;
+
+            std::cout << "arg.b_grid_desc_bk0_n_bk1_{" << arg.b_grid_desc_bk0_n_bk1_.GetLength(I0)
+                      << ", " << arg.b_grid_desc_bk0_n_bk1_.GetLength(I1) << ", "
+                      << arg.b_grid_desc_bk0_n_bk1_.GetLength(I2) << "}" << std::endl;
+
+            std::cout << "arg.e_grid_desc_m_n_{ " << arg.e_grid_desc_m_n_.GetLength(I0) << ", "
+                      << arg.e_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+        }
+#endif
+        if(ck::get_device_name() == "gfx908")
+        {
+            if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
+                           is_same_v<AccDataType, int32_t>))
+            {
+                return false;
+            }
+        }
+        else if(ck::get_device_name() == "gfx90a")
+        {
+            if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
+                           is_same_v<AccDataType, int32_t> || is_same_v<AccDataType, double>))
+            {
+                return false;
+            }
+        }
+        else
+        {
+            return false;
+        }
+
+        // tensors can't be bigger than 2GB each.
+        constexpr ck::long_index_t GB2 = (ck::long_index_t{1} << 31);
+
+        if(arg.a_grid_desc_ak0_m_ak1_.GetElementSpaceSize() * sizeof(ADataType) > GB2 ||
+           arg.b_grid_desc_bk0_n_bk1_.GetElementSpaceSize() * sizeof(BDataType) > GB2 ||
+           arg.e_grid_desc_m_n_.GetElementSpaceSize() * sizeof(EDataType) > GB2)
+        {
+            return false;
+        }
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            // check if it's 1x1, stride=1 conv
+            for(index_t i = 0; i < NDimSpatial; ++i)
+            {
+                if(!(arg.filter_spatial_lengths_[i] == 1 && arg.conv_filter_strides_[i] == 1 &&
+                     arg.input_left_pads_[i] == 0 && arg.input_right_pads_[i] == 0))
+                {
+                    return false;
+                }
+            }
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            // check if it's 1x1 conv
+            for(index_t i = 0; i < NDimSpatial; ++i)
+            {
+                if(!(arg.filter_spatial_lengths_[i] == 1 && arg.input_left_pads_[i] == 0 &&
+                     arg.input_right_pads_[i] == 0))
+                {
+                    return false;
+                }
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
+             arg.Conv_C_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store D/E matrix into global memory
+        if(!(arg.Conv_K_ % CDEBlockTransferScalarPerVector_NPerBlock == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.e_grid_desc_m_n_,
+                                           arg.block_2_etile_map_);
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const ADataType* p_in_grid,
+                             const BDataType* p_wei_grid,
+                             EDataType* p_out_grid,
+                             index_t N,
+                             index_t K,
+                             index_t C,
+                             std::vector<index_t> input_spatial_lengths,
+                             std::vector<index_t> filter_spatial_lengths,
+                             std::vector<index_t> output_spatial_lengths,
+                             std::vector<index_t> conv_filter_strides,
+                             std::vector<index_t> conv_filter_dilations,
+                             std::vector<index_t> input_left_pads,
+                             std::vector<index_t> input_right_pads,
+                             AElementwiseOperation in_element_op,
+                             BElementwiseOperation wei_element_op,
+                             CDEElementwiseOperation out_element_op)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const ADataType* p_in_grid,
+                        const BDataType* p_wei_grid,
+                        EDataType* p_out_grid,
+                        index_t N,
+                        index_t K,
+                        index_t C,
+                        std::vector<index_t> input_spatial_lengths,
+                        std::vector<index_t> filter_spatial_lengths,
+                        std::vector<index_t> output_spatial_lengths,
+                        std::vector<index_t> conv_filter_strides,
+                        std::vector<index_t> conv_filter_dilations,
+                        std::vector<index_t> input_left_pads,
+                        std::vector<index_t> input_right_pads,
+                        AElementwiseOperation in_element_op,
+                        BElementwiseOperation wei_element_op,
+                        CDEElementwiseOperation out_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<const ADataType*>(p_in_grid),
+                                          static_cast<const BDataType*>(p_wei_grid),
+                                          static_cast<EDataType*>(p_out_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConvFwdMultipleD_Xdl_CShuffle"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << getConvForwardSpecializationString(ConvForwardSpecialization)
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// Grouped Convolution Forword
+//   input : input image A[G, C, N, Hi, Wi],
+//   input : weight B[G, K, C, Y, X],
+//   input : D0[G, N, K, Ho, Wo], D1[G, N, K, Ho, Wo], ...
+//   output : output image E[G, N, K, Ho, Wo]
+//   C = a_op(A) * b_op(B)
+//   E = cde_op(C, D0, D1, ...)
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename ADataType,
+          typename BDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation>
+struct DeviceGroupedConvFwdMultipleD : public BaseOperator
+{
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        std::array<const void*, NumDTensor> p_ds,
+                        void* p_e,
+                        const std::vector<ck::index_t>& a_g_n_c_wis_lengths,
+                        const std::vector<ck::index_t>& a_g_n_c_wis_strides,
+                        const std::vector<ck::index_t>& b_g_k_c_xs_lengths,
+                        const std::vector<ck::index_t>& b_g_k_c_xs_strides,
+                        std::array<std::vector<ck::index_t>, NumDTensor> ds_g_n_k_wos_lengths;
+                        std::array<std::vector<ck::index_t>, NumDTensor> ds_g_n_k_wos_strides;
+                        const std::vector<ck::index_t>& e_g_n_k_wos_lengths,
+                        const std::vector<ck::index_t>& e_g_n_k_wos_strides,
+                        const std::vector<ck::index_t>& conv_filter_strides,
+                        const std::vector<ck::index_t>& conv_filter_dilations,
+                        const std::vector<ck::index_t>& input_left_pads,
+                        const std::vector<ck::index_t>& input_right_pads,
+                        const AElementwiseOperation& a_element_op,
+                        const BElementwiseOperation& b_element_op,
+                        const CDEElementwiseOperation& cde_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/gpu/device/device_convnd_fwd_multiple_d_nwc_kxc_nwk_xdl_cshuffle.hpp → include/ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp

@@ -13,11 +13,10 @@
 #include "ck/tensor_description/tensor_descriptor.hpp"
 #include "ck/tensor_description/tensor_descriptor_helper.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "ck/tensor_operation/gpu/device/device_conv_fwd_multiple_d.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d.hpp"
 #include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
 #include "ck/device_utility/device_prop.hpp"
 #include "ck/device_utility/kernel_launch.hpp"
@@ -110,6 +109,10 @@ __global__ void
 // out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
 //
 template <index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename ELayout,
           typename ADataType,
           typename BDataType,
           typename AccDataType,
@@ -150,31 +153,21 @@ template <index_t NDimSpatial,
           typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CDEBlockTransferScalarPerVector_NPerBlock,
           LoopScheduler LoopSched = make_default_loop_scheduler()>
-struct DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle
-    : public DeviceConvFwdMultipleD<
-          NDimSpatial,
-          ck::tuple_element_t<NDimSpatial - 1,
-                              ck::Tuple<ck::tensor_layout::convolution::NWC,
-                                        ck::tensor_layout::convolution::NHWC,
-                                        ck::tensor_layout::convolution::NDHWC>>,
-          ck::tuple_element_t<NDimSpatial - 1,
-                              ck::Tuple<ck::tensor_layout::convolution::KXC,
-                                        ck::tensor_layout::convolution::KYXC,
-                                        ck::tensor_layout::convolution::KZYXC>>,
-          ck::tuple_element_t<NDimSpatial - 1,
-                              ck::Tuple<ck::tensor_layout::convolution::NWK,
-                                        ck::tensor_layout::convolution::NHWK,
-                                        ck::tensor_layout::convolution::NDHWK>>,
-          ADataType,
-          BDataType,
-          DsDataType,
-          EDataType,
-          AElementwiseOperation,
-          BElementwiseOperation,
-          CDEElementwiseOperation>
+struct DeviceGroupedConvFwdMultipleD_Xdl_CShuffle
+    : public DeviceGroupedConvFwdMultipleD<NDimSpatial,
+                                           ALayout,
+                                           BLayout,
+                                           DsLayout,
+                                           ELayout,
+                                           ADataType,
+                                           BDataType,
+                                           DsDataType,
+                                           EDataType,
+                                           AElementwiseOperation,
+                                           BElementwiseOperation,
+                                           CDEElementwiseOperation>
 {
-
-    using DeviceOp = DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle;
+    using DeviceOp = DeviceGroupedConvFwdMultipleD_Xdl_CShuffle;
 
     static constexpr index_t NumDTensor = DsDataType::Size();
 
@@ -189,6 +182,7 @@ struct DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle
     static constexpr auto matrix_padder =
         MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
 
+    template <typename std::enable_if<ALayout, bool>::type = false>
     static auto GetWeightTensorDescriptor(index_t GemmNRaw, index_t GemmKRaw)
     {
         const auto wei_k_yxc_grid_desc =
@@ -1076,7 +1070,7 @@ struct DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle
         auto str = std::stringstream();
 
         // clang-format off
-        str << "DeviceConvNdFwdMultipleD_NwcKxcNwk_Xdl_CShuffle"
+        str << "DeviceGroupedConvFwdMultipleD_Xdl_CShuffle"
             << "<"
             << BlockSize << ", "
             << MPerBlock << ", "