Merge branch 'develop' into bwroblew/dpp8

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

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 

include/ck/tensor_operation/gpu/device/impl/device_avgpool3d_bwd_ndhwc_ndhwc.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#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/reduction_operator_mapping.hpp"
+#include "ck/tensor_operation/gpu/device/device_avgpool_bwd.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_threadwise.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// In and Din = [N, C, Di, Hi, Wi]
+// Out and Dout = [N, C, Do, Ho, Wo]
+// Out = AvgPoolFwd(In)
+// Din = AvgPoolBwd(Dout)
+// Pooling dimension = D, H, W
+template <typename DOutDataType,
+          typename DInDataType,
+          typename ComputeDataType,
+          ck::index_t BlockSize,
+          ck::index_t MThreadClusterSize,
+          ck::index_t KThreadClusterSize,
+          ck::index_t MThreadSliceSize,
+          ck::index_t KThreadSliceSize,
+          ck::index_t InSrcOutDstVectorSize>
+struct DeviceAvgPool3dBwd_NDHWC_NDHWC : public DeviceAvgPoolBwd<3,
+                                                                DOutDataType,
+                                                                DInDataType,
+                                                                tensor_layout::convolution::NDHWC,
+                                                                tensor_layout::convolution::NDHWC>
+{
+    static constexpr ck::index_t NDimSpatial = 3;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+
+    static constexpr ck::index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
+    static constexpr ck::index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
+
+    static auto
+    Make3DGridDescriptor_Out_M_K_In_M(const std::vector<ck::index_t>& dout_n_c_wos_lengths,
+                                      const std::vector<ck::index_t>& din_n_c_wos_length,
+                                      const std::vector<ck::index_t>& dout_n_c_wos_strides,
+                                      const std::vector<ck::index_t>& din_n_c_wos_strides,
+                                      const std::vector<ck::index_t>& window_lengths,
+                                      const std::vector<ck::index_t>& window_strides,
+                                      const std::vector<ck::index_t>& window_dilations,
+                                      const std::vector<ck::index_t>& input_left_pads,
+                                      const std::vector<ck::index_t>& input_right_pads,
+                                      const std::vector<ck::index_t>& tildes)
+    {
+        index_t i_ztilde = tildes[0];
+        index_t i_ytilde = tildes[1];
+        index_t i_xtilde = tildes[2];
+
+        const index_t N = dout_n_c_wos_lengths[0];
+        const index_t C = dout_n_c_wos_lengths[1];
+
+        const index_t Di = din_n_c_wos_length[2];
+        const index_t Hi = din_n_c_wos_length[3];
+        const index_t Wi = din_n_c_wos_length[4];
+
+        const index_t Do = dout_n_c_wos_lengths[2];
+        const index_t Ho = dout_n_c_wos_lengths[3];
+        const index_t Wo = dout_n_c_wos_lengths[4];
+
+        const index_t Z = window_lengths[0];
+        const index_t Y = window_lengths[1];
+        const index_t X = window_lengths[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 index_t ConvStrideD = window_strides[0];
+        const index_t ConvStrideH = window_strides[1];
+        const index_t ConvStrideW = window_strides[2];
+
+        const index_t ConvDilationD = window_dilations[0];
+        const index_t ConvDilationH = window_dilations[1];
+        const index_t ConvDilationW = window_dilations[2];
+
+        const auto out_n_do_ho_wo_c_grid_desc =
+            make_naive_tensor_descriptor(make_tuple(N, Do, Ho, Wo, C),
+                                         make_tuple(dout_n_c_wos_strides[0],
+                                                    dout_n_c_wos_strides[2],
+                                                    dout_n_c_wos_strides[3],
+                                                    dout_n_c_wos_strides[4],
+                                                    dout_n_c_wos_strides[1]));
+
+        const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
+        const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
+        const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+        const auto ZTilde = ConvStrideD / GcdStrideDilationD;
+        const auto YTilde = ConvStrideH / GcdStrideDilationH;
+        const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+        const auto ZDot = math::integer_divide_ceil(Z, ZTilde);
+        const auto YDot = math::integer_divide_ceil(Y, YTilde);
+        const auto XDot = math::integer_divide_ceil(X, XTilde);
+
+        const auto DTilde = Do + math::integer_divide_ceil(ConvDilationD * (Z - I1), ConvStrideD);
+        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 Tildes that contribute to non-padding area of input tensor
+        const auto IDTildeSliceBegin = math::integer_divide_floor(
+            math::max(I0, InLeftPadD - ConvDilationD * (ZTilde - I1)), ConvStrideD);
+        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 IDTildeSliceEnd =
+            math::min(DTilde, math::integer_divide_ceil(InLeftPadD + Di - I1, ConvStrideD) + 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 DTildeSlice = IDTildeSliceEnd - IDTildeSliceBegin;
+        const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+        const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
+
+        // ReduceK is different for each Reduce
+        const auto ZDotSlice = math::integer_divide_ceil(Z - i_ztilde, ZTilde);
+        const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+        const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+
+        // Problem size of reduction kernel
+        const index_t MRaw = N * DTildeSlice * HTildeSlice * WTildeSlice * C;
+        const index_t MPad = math::integer_least_multiple(MRaw, M_BlockTileSize) - MRaw;
+
+        const index_t KRaw = ZDotSlice * YDotSlice * XDotSlice;
+        const index_t KPad = math::integer_least_multiple(KRaw, K_BlockTileSize) - KRaw;
+
+        // Out[ReduceM, ReduceK]
+        const auto out_n_dop_hop_wop_c_grid_desc = transform_tensor_descriptor(
+            out_n_do_ho_wo_c_grid_desc,
+            make_tuple(make_pass_through_transform(N),
+                       make_pad_transform(Do, I0, I0),
+                       make_pad_transform(Ho, I0, I0),
+                       make_pad_transform(Wo, I0, I0),
+                       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 out_n_zdot_dtilde_ydot_htilde_xdot_wtilde_c_grid_desc =
+            transform_tensor_descriptor(
+                out_n_dop_hop_wop_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(ZDot, DTilde),
+                                         make_tuple(-ConvDilationD / GcdStrideDilationD, I1)),
+                    make_embed_transform(make_tuple(YDot, HTilde),
+                                         make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
+                    make_embed_transform(make_tuple(XDot, WTilde),
+                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
+                    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
+            out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_c_grid_desc =
+                transform_tensor_descriptor(
+                    out_n_zdot_dtilde_ydot_htilde_xdot_wtilde_c_grid_desc,
+                    make_tuple(make_pass_through_transform(N),
+                               make_slice_transform(ZDot, I0, ZDotSlice),
+                               make_slice_transform(DTilde, IDTildeSliceBegin, DTildeSlice),
+                               make_slice_transform(YDot, I0, YDotSlice),
+                               make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                               make_slice_transform(XDot, I0, XDotSlice),
+                               make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                               make_pass_through_transform(C)),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1>{},
+                               Sequence<2>{},
+                               Sequence<3>{},
+                               Sequence<4>{},
+                               Sequence<5>{},
+                               Sequence<6>{},
+                               Sequence<7>{}),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1>{},
+                               Sequence<2>{},
+                               Sequence<3>{},
+                               Sequence<4>{},
+                               Sequence<5>{},
+                               Sequence<6>{},
+                               Sequence<7>{}));
+
+        const auto out_grid_desc_reducemraw_reducekraw = transform_tensor_descriptor(
+            out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_c_grid_desc,
+            make_tuple(
+                make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice, C)),
+                make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice))),
+            make_tuple(Sequence<0, 2, 4, 6, 7>{}, Sequence<1, 3, 5>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        const auto out_grid_desc_reducem_reducek = transform_tensor_descriptor(
+            out_grid_desc_reducemraw_reducekraw,
+            make_tuple(make_right_pad_transform(MRaw, MPad), make_right_pad_transform(KRaw, KPad)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        // In[ReduceM]
+        const auto in_n_di_hi_wi_c_grid_desc =
+            make_naive_tensor_descriptor(make_tuple(N, Di, Hi, Wi, C),
+                                         make_tuple(din_n_c_wos_strides[0],
+                                                    din_n_c_wos_strides[2],
+                                                    din_n_c_wos_strides[3],
+                                                    din_n_c_wos_strides[4],
+                                                    din_n_c_wos_strides[1]));
+
+        const auto in_n_dip_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_ztilde_dtilde_ytilde_htilde_xtilde_wtilde_c_grid_desc =
+            transform_tensor_descriptor(
+                in_n_dip_hip_wip_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(XTilde, DTilde),
+                                                make_tuple(ConvDilationD, ConvStrideD)),
+                           make_embed_transform(make_tuple(YTilde, HTilde),
+                                                make_tuple(ConvDilationH, ConvStrideH)),
+                           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>{}, Sequence<4>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<1, 2>{},
+                           Sequence<3, 4>{},
+                           Sequence<5, 6>{},
+                           Sequence<7>{}));
+
+        const auto in_n_dtildeslice_htildeslice_wtildeslice_c_grid_desc =
+            transform_tensor_descriptor(
+                in_n_ztilde_dtilde_ytilde_htilde_xtilde_wtilde_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_freeze_transform(i_ztilde),
+                           make_slice_transform(DTilde, IDTildeSliceBegin, DTildeSlice),
+                           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>{},
+                           Sequence<2>{},
+                           Sequence<3>{},
+                           Sequence<4>{},
+                           Sequence<5>{},
+                           Sequence<6>{},
+                           Sequence<7>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<>{},
+                           Sequence<1>{},
+                           Sequence<>{},
+                           Sequence<2>{},
+                           Sequence<>{},
+                           Sequence<3>{},
+                           Sequence<4>{}));
+
+        const auto in_grid_desc_reducemraw = transform_tensor_descriptor(
+            in_n_dtildeslice_htildeslice_wtildeslice_c_grid_desc,
+            make_tuple(
+                make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice, C))),
+            make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+            make_tuple(Sequence<0>{}));
+
+        const auto in_grid_desc_reducem =
+            transform_tensor_descriptor(in_grid_desc_reducemraw,
+                                        make_tuple(make_right_pad_transform(MRaw, MPad)),
+                                        make_tuple(Sequence<0>{}),
+                                        make_tuple(Sequence<0>{}));
+
+        return make_tuple(out_grid_desc_reducem_reducek, in_grid_desc_reducem);
+    }
+
+    using DoutDinGridDesc = decltype(Make3DGridDescriptor_Out_M_K_In_M({0, 0, 0, 0, 0},
+                                                                       {0, 0, 0, 0, 0},
+                                                                       {0, 0, 0, 0, 0},
+                                                                       {0, 0, 0, 0, 0},
+                                                                       {0, 0, 0},
+                                                                       {0, 0, 0},
+                                                                       {0, 0, 0},
+                                                                       {0, 0, 0},
+                                                                       {0, 0, 0},
+                                                                       {0, 0, 0}));
+
+    using DoutGridDesc_M_K = remove_cvref_t<tuple_element_t<0, DoutDinGridDesc>>;
+    using DinGridDesc_M    = remove_cvref_t<tuple_element_t<1, DoutDinGridDesc>>;
+
+    // FIXME
+    // for NDHWC, the dim C is the fastest dimension, and is not reduced.
+    // Hence, it is in M dimension for reduction kernel.
+    static constexpr index_t OutSrcInDstVectorDim = 0; // 0: M, 1: K
+
+    using PassThrough = tensor_operation::element_wise::PassThrough;
+    using Div         = tensor_operation::element_wise::UnaryDivide;
+
+    using gridwise_reduce = GridwiseReduction_mk_to_m_threadwise<DOutDataType,
+                                                                 DInDataType,
+                                                                 ComputeDataType,
+                                                                 int,
+                                                                 DoutGridDesc_M_K,
+                                                                 DinGridDesc_M,
+                                                                 reduce::Add,
+                                                                 PassThrough,
+                                                                 Div,
+                                                                 InMemoryDataOperationEnum::Set,
+                                                                 false, // propagate_nan
+                                                                 BlockSize,
+                                                                 MThreadSliceSize,
+                                                                 KThreadSliceSize,
+                                                                 OutSrcInDstVectorDim,
+                                                                 InSrcOutDstVectorSize,
+                                                                 InSrcOutDstVectorSize>;
+
+    struct Argument : public BaseArgument
+    {
+        Argument(const DOutDataType* p_dout,
+                 DInDataType* p_din,
+                 std::vector<ck::index_t> dout_n_c_wos_lengths,
+                 std::vector<ck::index_t> din_n_c_wos_length,
+                 std::vector<ck::index_t> dout_n_c_wos_strides,
+                 std::vector<ck::index_t> din_n_c_wos_strides,
+                 std::vector<ck::index_t> window_lengths,
+                 std::vector<ck::index_t> window_strides,
+                 std::vector<ck::index_t> window_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads)
+            : p_dout_grid_{p_dout},
+              p_din_grid_{p_din},
+              dout_n_c_wos_lengths_{dout_n_c_wos_lengths},
+              din_n_c_wos_length_{din_n_c_wos_length},
+              dout_n_c_wos_strides_{dout_n_c_wos_strides},
+              din_n_c_wos_strides_{din_n_c_wos_strides},
+              num_reduce_{1},
+              div_element_op_{window_lengths[0] * window_lengths[1] * window_lengths[2]}
+        {
+            std::vector<ck::index_t> Tildes(NDimSpatial);
+            for(int i = 0; i < NDimSpatial; ++i)
+            {
+                int GcdStrideDilation = math::gcd(window_strides[i], window_dilations[i]);
+                Tildes[i]             = window_strides[i] / GcdStrideDilation;
+                num_reduce_ *= Tildes[i];
+            }
+
+            for(index_t i_ztilde = 0; i_ztilde < Tildes[0]; ++i_ztilde)
+            {
+                for(index_t i_ytilde = 0; i_ytilde < Tildes[1]; ++i_ytilde)
+                {
+                    for(index_t i_xtilde = 0; i_xtilde < Tildes[2]; ++i_xtilde)
+                    {
+                        // check slice is valid
+                        const auto ZDotSlice =
+                            math::integer_divide_ceil(window_lengths[0] - i_ztilde, Tildes[0]);
+                        const auto YDotSlice =
+                            math::integer_divide_ceil(window_lengths[1] - i_ytilde, Tildes[1]);
+                        const auto XDotSlice =
+                            math::integer_divide_ceil(window_lengths[2] - i_xtilde, Tildes[2]);
+
+                        if(ZDotSlice * YDotSlice * XDotSlice <= 0)
+                        {
+                            continue;
+                        }
+
+                        const auto dout_din_grid_desc =
+                            Make3DGridDescriptor_Out_M_K_In_M(dout_n_c_wos_lengths,
+                                                              din_n_c_wos_length,
+                                                              dout_n_c_wos_strides,
+                                                              din_n_c_wos_strides,
+                                                              window_lengths,
+                                                              window_strides,
+                                                              window_dilations,
+                                                              input_left_pads,
+                                                              input_right_pads,
+                                                              {i_ztilde, i_ytilde, i_xtilde});
+
+                        dout_grid_desc_m_k_container_.push_back(dout_din_grid_desc[I0]);
+                        din_grid_desc_m_container_.push_back(dout_din_grid_desc[I1]);
+                    }
+                }
+            }
+        }
+
+        const DOutDataType* p_dout_grid_;
+        DInDataType* p_din_grid_;
+        std::vector<ck::index_t> dout_n_c_wos_lengths_;
+        std::vector<ck::index_t> din_n_c_wos_length_;
+        std::vector<ck::index_t> dout_n_c_wos_strides_;
+        std::vector<ck::index_t> din_n_c_wos_strides_;
+
+        int num_reduce_;
+        std::vector<DoutGridDesc_M_K> dout_grid_desc_m_k_container_;
+        std::vector<DinGridDesc_M> din_grid_desc_m_container_;
+
+        Div div_element_op_;
+    };
+
+    struct Invoker : public BaseInvoker
+    {
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            float ave_time = 0;
+
+            for(index_t i = 0; i < arg.num_reduce_; i++)
+            {
+                const auto kernel = kernel_reduce_threadwise<gridwise_reduce,
+                                                             false,
+                                                             false,
+                                                             false, // don't have index input
+                                                             DOutDataType,
+                                                             DInDataType,
+                                                             ComputeDataType,
+                                                             int,
+                                                             DoutGridDesc_M_K,
+                                                             DinGridDesc_M,
+                                                             PassThrough,
+                                                             Div>;
+
+                ck::index_t M           = arg.dout_grid_desc_m_k_container_[i].GetLength(I0);
+                const index_t grid_size = (M / M_BlockTileSize);
+
+                ave_time += launch_and_time_kernel(stream_config,
+                                                   kernel,
+                                                   dim3(grid_size),
+                                                   dim3(BlockSize),
+                                                   0,
+                                                   arg.dout_grid_desc_m_k_container_[i],
+                                                   arg.din_grid_desc_m_container_[i],
+                                                   PassThrough{},
+                                                   arg.div_element_op_,
+                                                   float(1),
+                                                   arg.p_dout_grid_,
+                                                   nullptr,
+                                                   float(0),
+                                                   arg.p_din_grid_,
+                                                   nullptr);
+            }
+
+            return ave_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)
+    {
+        constexpr index_t Rank = NDimSpatial + 2;
+        int doutFastestDim     = -1;
+        int dinFastestDim      = -1;
+
+        for(int i = 0; i < Rank; ++i)
+        {
+            if(arg.dout_n_c_wos_strides_[i] == 1)
+                doutFastestDim = i;
+            if(arg.din_n_c_wos_strides_[i] == 1)
+                dinFastestDim = i;
+        }
+
+        if(doutFastestDim == -1 || dinFastestDim == -1)
+        {
+            if constexpr(InSrcOutDstVectorSize != 1)
+                return false;
+        }
+        else
+        {
+            if(arg.dout_n_c_wos_lengths_[doutFastestDim] % InSrcOutDstVectorSize != 0)
+                return false;
+            if(arg.din_n_c_wos_length_[dinFastestDim] % InSrcOutDstVectorSize != 0)
+                return false;
+        }
+
+        return true;
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_dout,
+                        void* p_din,
+                        std::vector<ck::index_t> dout_n_c_wos_lengths,
+                        std::vector<ck::index_t> din_n_c_wos_length,
+                        std::vector<ck::index_t> dout_n_c_wos_strides,
+                        std::vector<ck::index_t> din_n_c_wos_strides,
+                        std::vector<ck::index_t> window_lengths,
+                        std::vector<ck::index_t> window_strides,
+                        std::vector<ck::index_t> window_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads) override
+    {
+        constexpr index_t Rank = NDimSpatial + 2;
+
+        if(dout_n_c_wos_strides.size() != Rank || din_n_c_wos_strides.size() != Rank ||
+           dout_n_c_wos_lengths.size() != Rank || din_n_c_wos_length.size() != Rank)
+            throw std::runtime_error("dimension is incorrect");
+
+        if(window_lengths.size() != NDimSpatial || window_strides.size() != NDimSpatial ||
+           window_dilations.size() != NDimSpatial || input_left_pads.size() != NDimSpatial ||
+           input_right_pads.size() != NDimSpatial)
+            throw std::runtime_error("dimension is incorrect");
+
+        return std::make_unique<Argument>(static_cast<const DOutDataType*>(p_dout),
+                                          static_cast<DInDataType*>(p_din),
+                                          dout_n_c_wos_lengths,
+                                          din_n_c_wos_length,
+                                          dout_n_c_wos_strides,
+                                          din_n_c_wos_strides,
+                                          window_lengths,
+                                          window_strides,
+                                          window_dilations,
+                                          input_left_pads,
+                                          input_right_pads);
+    }
+
+    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 << "DeviceAvgPool3dBwd<" << BlockSize << ",";
+        str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
+        str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
+        str <<"InSrcOutDstVectorSize_" << InSrcOutDstVectorSize << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/gpu/device/impl/device_cgemm_4gemm_xdl_cshuffle.hpp

@@ -123,7 +123,8 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
         ALayout,
         BLayout,
         CLayout,
-        ADataType, // TODO: distinguish A/B datatype
+        ADataType,
+        BDataType,
         GemmAccDataType,
         CShuffleDataType,
         CDataType,
@@ -284,8 +285,11 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
 
             if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
             {
-                const auto kernel =
-                    kernel_gemm_xdl_cshuffle_v1<GridwiseGemm, ADataType, CDataType, true>;
+                const auto kernel = kernel_gemm_xdl_cshuffle_v1<GridwiseGemm,
+                                                                ADataType,
+                                                                BDataType,
+                                                                CDataType,
+                                                                true>;
 
                 ave_time += launch_and_time_kernel(stream_config,
                                                    kernel,
@@ -357,8 +361,11 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
             }
             else
             {
-                const auto kernel =
-                    kernel_gemm_xdl_cshuffle_v1<GridwiseGemm, ADataType, CDataType, false>;
+                const auto kernel = kernel_gemm_xdl_cshuffle_v1<GridwiseGemm,
+                                                                ADataType,
+                                                                BDataType,
+                                                                CDataType,
+                                                                false>;
 
                 ave_time += launch_and_time_kernel(stream_config,
                                                    kernel,

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

@@ -65,7 +65,8 @@ template <typename ALayout,
           typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
           LoopScheduler LoopSched     = make_default_loop_scheduler(),
-          PipelineVersion PipelineVer = PipelineVersion::v1>
+          PipelineVersion PipelineVer = PipelineVersion::v1,
+          typename ComputeType        = CDataType>
 struct DeviceGemm_Xdl_CShuffle : public DeviceGemm<ALayout,
                                                    BLayout,
                                                    CLayout,
@@ -87,7 +88,8 @@ struct DeviceGemm_Xdl_CShuffle : public DeviceGemm<ALayout,
         ALayout,
         BLayout,
         CLayout,
-        ADataType, // TODO: distinguish A/B datatype
+        ADataType,
+        BDataType,
         GemmAccDataType,
         CShuffleDataType,
         CDataType,
@@ -128,7 +130,8 @@ struct DeviceGemm_Xdl_CShuffle : public DeviceGemm<ALayout,
         CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
         CShuffleBlockTransferScalarPerVector_NPerBlock,
         LoopSched,
-        PipelineVer>;
+        PipelineVer,
+        ComputeType>;
 
     using Argument = typename GridwiseGemm::Argument;
 

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

@@ -784,15 +784,12 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
         Argument(const InDataType* p_in_grid,
                  WeiDataType* p_wei_grid,
                  const OutDataType* p_out_grid,
-                 const ck::index_t G,
-                 const ck::index_t N,
-                 const ck::index_t K,
-                 const ck::index_t C,
-                 const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-                 const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-                 const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-                 const std::array<ck::index_t, NDimSpatial + 3>& /*input_strides*/,
-                 const std::array<ck::index_t, NDimSpatial + 3>& /*output_strides*/,
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
+                 const std::array<index_t, NDimSpatial + 3>& /*a_g_n_c_wis_strides*/,
+                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
+                 const std::array<index_t, NDimSpatial + 3>& /*b_g_k_c_xs_strides*/,
+                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
+                 const std::array<index_t, NDimSpatial + 3>& /*e_g_n_k_wos_strides*/,
                  const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
                  const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
                  const std::array<ck::index_t, NDimSpatial>& input_left_pads,
@@ -812,27 +809,38 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
               a_element_op_{out_element_op},
               b_element_op_{wei_element_op},
               c_element_op_{in_element_op},
-              Conv_G_{G},
-              Conv_N_{N},
-              Conv_K_{K},
-              Conv_C_{C},
-              input_spatial_lengths_{input_spatial_lengths},
-              filter_spatial_lengths_{filter_spatial_lengths},
-              output_spatial_lengths_{output_spatial_lengths},
+              Conv_G_{a_g_n_c_wis_lengths[0]},
+              Conv_N_{a_g_n_c_wis_lengths[1]},
+              Conv_K_{b_g_k_c_xs_lengths[1]},
+              Conv_C_{a_g_n_c_wis_lengths[2]},
+              input_spatial_lengths_{},
+              filter_spatial_lengths_{},
+              output_spatial_lengths_{},
               conv_filter_strides_{conv_filter_strides},
               conv_filter_dilations_{conv_filter_dilations},
               input_left_pads_{input_left_pads},
               input_right_pads_{input_right_pads},
               k_batch_{split_k}
         {
+            constexpr index_t spatial_offset = 3;
+            std::copy(begin(a_g_n_c_wis_lengths) + spatial_offset,
+                      end(a_g_n_c_wis_lengths),
+                      begin(input_spatial_lengths_));
+            std::copy(begin(b_g_k_c_xs_lengths) + spatial_offset,
+                      end(b_g_k_c_xs_lengths),
+                      begin(filter_spatial_lengths_));
+            std::copy(begin(e_g_n_k_wos_lengths) + spatial_offset,
+                      end(e_g_n_k_wos_lengths),
+                      begin(output_spatial_lengths_));
+
             const auto descs =
                 DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
-                    N,
-                    K,
-                    C,
-                    input_spatial_lengths,
-                    filter_spatial_lengths,
-                    output_spatial_lengths,
+                    Conv_N_,
+                    Conv_K_,
+                    Conv_C_,
+                    input_spatial_lengths_,
+                    filter_spatial_lengths_,
+                    output_spatial_lengths_,
                     conv_filter_strides,
                     conv_filter_dilations,
                     input_left_pads,
@@ -856,21 +864,21 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
 
             // A/B/C Batch Stride
             compute_ptr_offset_of_batch_.BatchStrideA_ =
-                N * K *
-                std::accumulate(begin(output_spatial_lengths),
-                                end(output_spatial_lengths),
+                Conv_N_ * Conv_K_ *
+                std::accumulate(begin(output_spatial_lengths_),
+                                end(output_spatial_lengths_),
                                 index_t{1},
                                 std::multiplies<>{});
             compute_ptr_offset_of_batch_.BatchStrideB_ =
-                N * C *
-                std::accumulate(begin(input_spatial_lengths),
-                                end(input_spatial_lengths),
+                Conv_N_ * Conv_C_ *
+                std::accumulate(begin(input_spatial_lengths_),
+                                end(input_spatial_lengths_),
                                 index_t{1},
                                 std::multiplies<>{});
             compute_ptr_offset_of_batch_.BatchStrideC_ =
-                K * C *
-                std::accumulate(begin(filter_spatial_lengths),
-                                end(filter_spatial_lengths),
+                Conv_K_ * Conv_C_ *
+                std::accumulate(begin(filter_spatial_lengths_),
+                                end(filter_spatial_lengths_),
                                 index_t{1},
                                 std::multiplies<>{});
         }
@@ -904,9 +912,9 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
         const index_t Conv_K_;
         const index_t Conv_C_;
 
-        const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths_;
-        const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths_;
-        const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> input_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> filter_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> output_spatial_lengths_;
         const std::array<ck::index_t, NDimSpatial>& conv_filter_strides_;
         const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations_;
         const std::array<ck::index_t, NDimSpatial>& input_left_pads_;
@@ -1110,39 +1118,34 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
         return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
     }
 
-    static auto MakeArgument(const InDataType* p_in_grid,
-                             WeiDataType* p_wei_grid,
-                             const OutDataType* p_out_grid,
-                             const ck::index_t G,
-                             const ck::index_t N,
-                             const ck::index_t K,
-                             const ck::index_t C,
-                             const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-                             const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-                             const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-                             const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
-                             const std::array<ck::index_t, NDimSpatial + 3>& output_strides,
-                             const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
-                             const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
-                             const std::array<ck::index_t, NDimSpatial>& input_left_pads,
-                             const std::array<ck::index_t, NDimSpatial>& input_right_pads,
-                             InElementwiseOperation in_element_op,
-                             WeiElementwiseOperation wei_element_op,
-                             OutElementwiseOperation out_element_op,
-                             ck::index_t split_k)
+    static auto
+    MakeArgument(const InDataType* p_in_grid,
+                 WeiDataType* p_wei_grid,
+                 const OutDataType* p_out_grid,
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
+                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
+                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
+                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
+                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+                 const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+                 const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op,
+                 ck::index_t split_k)
     {
         return Argument{p_in_grid,
                         p_wei_grid,
                         p_out_grid,
-                        G,
-                        N,
-                        K,
-                        C,
-                        input_spatial_lengths,
-                        filter_spatial_lengths,
-                        output_spatial_lengths,
-                        input_strides,
-                        output_strides,
+                        a_g_n_c_wis_lengths, // input
+                        a_g_n_c_wis_strides,
+                        b_g_k_c_xs_lengths, // weight
+                        b_g_k_c_xs_strides,
+                        e_g_n_k_wos_lengths, // output
+                        e_g_n_k_wos_strides,
                         conv_filter_strides,
                         conv_filter_dilations,
                         input_left_pads,
@@ -1159,15 +1162,12 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
     MakeArgumentPointer(const void* p_in_grid,
                         void* p_wei_grid,
                         const void* p_out_grid,
-                        const ck::index_t G,
-                        const ck::index_t N,
-                        const ck::index_t K,
-                        const ck::index_t C,
-                        const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-                        const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-                        const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-                        const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
-                        const std::array<ck::index_t, NDimSpatial + 3>& output_strides,
+                        const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
+                        const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
+                        const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
+                        const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
+                        const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
+                        const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
                         const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
                         const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
                         const std::array<ck::index_t, NDimSpatial>& input_left_pads,
@@ -1180,15 +1180,12 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
         return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
                                           static_cast<WeiDataType*>(p_wei_grid),
                                           static_cast<const OutDataType*>(p_out_grid),
-                                          G,
-                                          N,
-                                          K,
-                                          C,
-                                          input_spatial_lengths,
-                                          filter_spatial_lengths,
-                                          output_spatial_lengths,
-                                          input_strides,
-                                          output_strides,
+                                          a_g_n_c_wis_lengths, // input
+                                          a_g_n_c_wis_strides,
+                                          b_g_k_c_xs_lengths, // weight
+                                          b_g_k_c_xs_strides,
+                                          e_g_n_k_wos_lengths, // output
+                                          e_g_n_k_wos_strides,
                                           conv_filter_strides,
                                           conv_filter_dilations,
                                           input_left_pads,

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

@@ -245,21 +245,10 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                        const ck::index_t K,
                        const std::array<ck::index_t, NDimSpatial + 3>& output_strides)
     {
-        if constexpr(is_GNHWK_GKYXC_GNHWC)
-        {
-            return make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
-        }
-        else if constexpr(is_NHWGK_GKYXC_NHWGC)
-        {
-            const index_t WoStride = output_strides[4];
-            const auto KStride     = Number<1>{};
-            return make_naive_tensor_descriptor(make_tuple(N * Ho * Wo, K),
-                                                make_tuple(WoStride, KStride));
-        }
-        else
-        {
-            throw std::runtime_error("wrong! unsupported layout: " + OutLayout::name());
-        }
+        const index_t WoStride = output_strides[4];
+        const auto KStride     = Number<1>{};
+        return make_naive_tensor_descriptor(make_tuple(N * Ho * Wo, K),
+                                            make_tuple(WoStride, KStride));
     }
 
     template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
@@ -270,42 +259,36 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                       const ck::index_t C,
                       const std::array<ck::index_t, NDimSpatial + 3>& input_strides)
     {
-        if constexpr(is_GNHWK_GKYXC_GNHWC)
-        {
-            if constexpr(ConvBackwardWeightSpecialization ==
-                         ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
-            {
-                return make_naive_tensor_descriptor_packed(make_tuple(N * Hi * Wi, C));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
-            }
-        }
-        else if constexpr(is_NHWGK_GKYXC_NHWGC)
+        const index_t NStride  = input_strides[1];
+        const index_t HiStride = input_strides[3];
+        const index_t WiStride = input_strides[4];
+        const auto CStride     = input_strides[2];
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
         {
-            const index_t NStride  = input_strides[1];
-            const index_t HiStride = input_strides[3];
-            const index_t WiStride = input_strides[4];
-            const auto CStride     = input_strides[2];
-            if constexpr(ConvBackwardWeightSpecialization ==
-                         ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
-            {
-                return make_naive_tensor_descriptor(make_tuple(N * Hi * Wi, C),
-                                                    make_tuple(WiStride, CStride));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(N, Hi, Wi, C), make_tuple(NStride, HiStride, WiStride, CStride));
-            }
+            return make_naive_tensor_descriptor(make_tuple(N * Hi * Wi, C),
+                                                make_tuple(WiStride, CStride));
         }
         else
         {
-            throw std::runtime_error("wrong! unsupported layout: " + InLayout::name());
+            return make_naive_tensor_descriptor(make_tuple(N, Hi, Wi, C),
+                                                make_tuple(NStride, HiStride, WiStride, CStride));
         }
     }
 
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
+    constexpr static auto
+    make_wei_grid_desc(const ck::index_t K,
+                       const ck::index_t Y,
+                       const ck::index_t X,
+                       const ck::index_t C,
+                       const std::array<ck::index_t, NDimSpatial + 3>& weights_strides)
+    {
+        const auto CStride = Number<1>{};
+        const auto KStride = weights_strides[1];
+        return make_naive_tensor_descriptor(make_tuple(K, Y * X * C), make_tuple(KStride, CStride));
+    }
+
     template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
     constexpr static auto
     make_out_grid_desc(const ck::index_t N,
@@ -315,21 +298,10 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                        const ck::index_t K,
                        const std::array<ck::index_t, NDimSpatial + 3>& output_strides)
     {
-        if constexpr(is_GNDHWK_GKZYXC_GNDHWC)
-        {
-            return make_naive_tensor_descriptor_packed(make_tuple(N * Do * Ho * Wo, K));
-        }
-        else if constexpr(is_NDHWGK_GKZYXC_NDHWGC)
-        {
-            const index_t WoStride = output_strides[5];
-            const auto KStride     = Number<1>{};
-            return make_naive_tensor_descriptor(make_tuple(N * Do * Ho * Wo, K),
-                                                make_tuple(WoStride, KStride));
-        }
-        else
-        {
-            throw std::runtime_error("wrong! unsupported layout: " + OutLayout::name());
-        }
+        const index_t WoStride = output_strides[5];
+        const auto KStride     = Number<1>{};
+        return make_naive_tensor_descriptor(make_tuple(N * Do * Ho * Wo, K),
+                                            make_tuple(WoStride, KStride));
     }
 
     template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
@@ -341,44 +313,40 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                       const ck::index_t C,
                       const std::array<ck::index_t, NDimSpatial + 3>& input_strides)
     {
-        if constexpr(is_GNDHWK_GKZYXC_GNDHWC)
+        const index_t NStride  = input_strides[1];
+        const index_t DiStride = input_strides[3];
+        const index_t HiStride = input_strides[4];
+        const index_t WiStride = input_strides[5];
+        const auto CStride     = input_strides[2];
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
         {
-            if constexpr(ConvBackwardWeightSpecialization ==
-                         ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
-            {
-                return make_naive_tensor_descriptor_packed(make_tuple(N * Di * Hi * Wi, C));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));
-            }
-        }
-        else if constexpr(is_NDHWGK_GKZYXC_NDHWGC)
-        {
-            const index_t NStride  = input_strides[1];
-            const index_t DiStride = input_strides[3];
-            const index_t HiStride = input_strides[4];
-            const index_t WiStride = input_strides[5];
-            const auto CStride     = input_strides[2];
-            if constexpr(ConvBackwardWeightSpecialization ==
-                         ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
-            {
-                return make_naive_tensor_descriptor(make_tuple(N * Di * Hi * Wi, C),
-                                                    make_tuple(WiStride, CStride));
-            }
-            else
-            {
-                return make_naive_tensor_descriptor(
-                    make_tuple(N, Di, Hi, Wi, C),
-                    make_tuple(NStride, DiStride, HiStride, WiStride, CStride));
-            }
+            return make_naive_tensor_descriptor(make_tuple(N * Di * Hi * Wi, C),
+                                                make_tuple(WiStride, CStride));
         }
         else
         {
-            throw std::runtime_error("wrong! unsupported layout: " + InLayout::name());
+            return make_naive_tensor_descriptor(
+                make_tuple(N, Di, Hi, Wi, C),
+                make_tuple(NStride, DiStride, HiStride, WiStride, CStride));
         }
     }
 
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
+    constexpr static auto
+    make_wei_grid_desc(const ck::index_t K,
+                       const ck::index_t Z,
+                       const ck::index_t Y,
+                       const ck::index_t X,
+                       const ck::index_t C,
+                       const std::array<ck::index_t, NDimSpatial + 3>& weights_strides)
+    {
+        const auto CStride = Number<1>{};
+        const auto KStride = weights_strides[1];
+        return make_naive_tensor_descriptor(make_tuple(K, Z * Y * X * C),
+                                            make_tuple(KStride, CStride));
+    }
+
     template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
     static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
         const ck::index_t N,
@@ -388,6 +356,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
         const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
         const std::array<ck::index_t, NDimSpatial + 3>& /* input_strides */,
+        const std::array<ck::index_t, NDimSpatial + 3>& /* weights_strides */,
         const std::array<ck::index_t, NDimSpatial + 3>& /* output_strides */,
         const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
         const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
@@ -409,6 +378,9 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         const index_t GemmM      = K;
         const index_t GemmN      = C * X;
 
+        const auto PadGemmM = (MPerBlock - GemmM % MPerBlock) % MPerBlock;
+        const auto PadGemmN = (NPerBlock - GemmN % NPerBlock) % NPerBlock;
+
         const index_t GemmKBatch = batch_k;
         const index_t GemmK0 =
             math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
@@ -527,9 +499,37 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
             const auto wei_gemmm_gemmn_grid_desc =
                 make_naive_tensor_descriptor_packed(make_tuple(K, X * C));
 
-            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
-                              in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+            // Padd
+            const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmM, PadGemmM),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmN, PadGemmN),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto wei_gemmm_gemmn_pad_grid_desc =
+                transform_tensor_descriptor(wei_gemmm_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmM, PadGemmM),
+                                                       make_right_pad_transform(GemmN, PadGemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc,
+                              in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc,
+                              wei_gemmm_gemmn_pad_grid_desc);
         }
     }
 
@@ -542,6 +542,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
         const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
         const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
+        const std::array<ck::index_t, NDimSpatial + 3>& weights_strides,
         const std::array<ck::index_t, NDimSpatial + 3>& output_strides,
         const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
         const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
@@ -576,6 +577,9 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         const index_t GemmM      = K;
         const index_t GemmN      = C * X * Y;
 
+        const auto PadGemmM = (MPerBlock - GemmM % MPerBlock) % MPerBlock;
+        const auto PadGemmN = (NPerBlock - GemmN % NPerBlock) % NPerBlock;
+
         const index_t GemmKBatch = batch_k;
         const index_t GemmK0 =
             math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
@@ -584,6 +588,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
 
         const auto out_grid_desc = make_out_grid_desc<NDim>(N, Ho, Wo, K, output_strides);
         const auto in_grid_desc  = make_in_grid_desc<NDim>(N, Hi, Wi, C, input_strides);
+        const auto wei_grid_desc = make_wei_grid_desc<NDim>(K, Y, X, C, weights_strides);
 
         if constexpr(ConvBackwardWeightSpecialization ==
                      ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
@@ -618,13 +623,9 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
-            // C: weight tensor
-            const auto wei_gemmm_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
-
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_grid_desc);
         }
         else
         {
@@ -684,13 +685,37 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
-            // C: weight tensor
-            const auto wei_gemmm_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
-
-            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
-                              in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+            // Padd
+            const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmM, PadGemmM),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmN, PadGemmN),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto wei_gemmm_gemmn_pad_grid_desc =
+                transform_tensor_descriptor(wei_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmM, PadGemmM),
+                                                       make_right_pad_transform(GemmN, PadGemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc,
+                              in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc,
+                              wei_gemmm_gemmn_pad_grid_desc);
         }
     }
 
@@ -703,6 +728,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
         const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
         const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
+        const std::array<ck::index_t, NDimSpatial + 3>& weights_strides,
         const std::array<ck::index_t, NDimSpatial + 3>& output_strides,
         const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
         const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
@@ -744,6 +770,9 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         const index_t GemmM      = K;
         const index_t GemmN      = C * Z * X * Y;
 
+        const auto PadGemmM = (MPerBlock - GemmM % MPerBlock) % MPerBlock;
+        const auto PadGemmN = (NPerBlock - GemmN % NPerBlock) % NPerBlock;
+
         const index_t GemmKBatch = batch_k;
         const index_t GemmK0 =
             math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
@@ -752,6 +781,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
 
         const auto out_grid_desc = make_out_grid_desc<NDim>(N, Do, Ho, Wo, K, output_strides);
         const auto in_grid_desc  = make_in_grid_desc<NDim>(N, Di, Hi, Wi, C, input_strides);
+        const auto wei_grid_desc = make_wei_grid_desc<NDim>(K, Z, Y, X, C, weights_strides);
 
         if constexpr(ConvBackwardWeightSpecialization ==
                      ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
@@ -786,13 +816,9 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
-            // C: weight tensor
-            const auto wei_gemmm_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(K, Z * Y * X * C));
-
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_grid_desc);
         }
         else
         {
@@ -861,13 +887,37 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
-            // C: weight tensor
-            const auto wei_gemmm_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(K, Z * Y * X * C));
-
-            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
-                              in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+            // Padd
+            const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmM, PadGemmM),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmN, PadGemmN),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto wei_gemmm_gemmn_pad_grid_desc =
+                transform_tensor_descriptor(wei_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmM, PadGemmM),
+                                                       make_right_pad_transform(GemmN, PadGemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc,
+                              in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc,
+                              wei_gemmm_gemmn_pad_grid_desc);
         }
     } // function end
 
@@ -887,6 +937,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                                                                   lengths,
                                                                   strides,
                                                                   strides,
+                                                                  strides,
                                                                   params,
                                                                   params,
                                                                   params,
@@ -910,6 +961,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                                                                   lengths,
                                                                   strides,
                                                                   strides,
+                                                                  strides,
                                                                   params,
                                                                   params,
                                                                   params,
@@ -933,6 +985,7 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                                                                   lengths,
                                                                   strides,
                                                                   strides,
+                                                                  strides,
                                                                   params,
                                                                   params,
                                                                   params,
@@ -1051,15 +1104,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         Argument(const InDataType* p_in_grid,
                  WeiDataType* p_wei_grid,
                  const OutDataType* p_out_grid,
-                 const ck::index_t G,
-                 const ck::index_t N,
-                 const ck::index_t K,
-                 const ck::index_t C,
-                 const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-                 const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-                 const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-                 const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
-                 const std::array<ck::index_t, NDimSpatial + 3>& output_strides,
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
+                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
+                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
+                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
+                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
                  const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
                  const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
                  const std::array<ck::index_t, NDimSpatial>& input_left_pads,
@@ -1084,27 +1134,40 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
               a_element_op_{out_element_op},
               b_element_op_{in_element_op},
               c_element_op_{wei_element_op},
-              Conv_G_{G},
-              Conv_N_{N},
-              Conv_K_{K},
-              Conv_C_{C},
-              output_spatial_lengths_{output_spatial_lengths},
-              filter_spatial_lengths_{filter_spatial_lengths},
+              Conv_G_{a_g_n_c_wis_lengths[0]},
+              Conv_N_{a_g_n_c_wis_lengths[1]},
+              Conv_K_{b_g_k_c_xs_lengths[1]},
+              Conv_C_{a_g_n_c_wis_lengths[2]},
+              input_spatial_lengths_{},
+              filter_spatial_lengths_{},
+              output_spatial_lengths_{},
               conv_filter_strides_{conv_filter_strides},
               input_left_pads_{input_left_pads},
               input_right_pads_{input_right_pads},
               k_batch_{split_k}
         {
+            constexpr index_t spatial_offset = 3;
+            std::copy(begin(a_g_n_c_wis_lengths) + spatial_offset,
+                      end(a_g_n_c_wis_lengths),
+                      begin(input_spatial_lengths_));
+            std::copy(begin(b_g_k_c_xs_lengths) + spatial_offset,
+                      end(b_g_k_c_xs_lengths),
+                      begin(filter_spatial_lengths_));
+            std::copy(begin(e_g_n_k_wos_lengths) + spatial_offset,
+                      end(e_g_n_k_wos_lengths),
+                      begin(output_spatial_lengths_));
+
             const auto descs =
                 DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
-                    N,
-                    K,
-                    C,
-                    input_spatial_lengths,
-                    filter_spatial_lengths,
-                    output_spatial_lengths,
-                    input_strides,
-                    output_strides,
+                    Conv_N_,
+                    Conv_K_,
+                    Conv_C_,
+                    input_spatial_lengths_,
+                    filter_spatial_lengths_,
+                    output_spatial_lengths_,
+                    a_g_n_c_wis_strides,
+                    b_g_k_c_xs_strides,
+                    e_g_n_k_wos_strides,
                     conv_filter_strides,
                     conv_filter_dilations,
                     input_left_pads,
@@ -1119,12 +1182,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                 GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
 
             // A/B/C Batch Stride
-            compute_ptr_offset_of_batch_.BatchStrideA_ = output_strides[0];
-            compute_ptr_offset_of_batch_.BatchStrideB_ = input_strides[0];
+            compute_ptr_offset_of_batch_.BatchStrideA_ = e_g_n_k_wos_strides[0];
+            compute_ptr_offset_of_batch_.BatchStrideB_ = a_g_n_c_wis_strides[0];
             compute_ptr_offset_of_batch_.BatchStrideC_ =
-                K * C *
-                std::accumulate(begin(filter_spatial_lengths),
-                                end(filter_spatial_lengths),
+                Conv_K_ * Conv_C_ *
+                std::accumulate(begin(filter_spatial_lengths_),
+                                end(filter_spatial_lengths_),
                                 index_t{1},
                                 std::multiplies<>{});
 
@@ -1163,8 +1226,9 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         const index_t Conv_N_;
         const index_t Conv_K_;
         const index_t Conv_C_;
-        const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths_;
-        const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> input_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> filter_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> output_spatial_lengths_;
         const std::array<ck::index_t, NDimSpatial>& conv_filter_strides_;
         const std::array<ck::index_t, NDimSpatial>& input_left_pads_;
         const std::array<ck::index_t, NDimSpatial>& input_right_pads_;
@@ -1339,39 +1403,34 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
     }
 
-    static auto MakeArgument(const InDataType* p_in_grid,
-                             WeiDataType* p_wei_grid,
-                             const OutDataType* p_out_grid,
-                             const ck::index_t G,
-                             const ck::index_t N,
-                             const ck::index_t K,
-                             const ck::index_t C,
-                             const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-                             const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-                             const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-                             const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
-                             const std::array<ck::index_t, NDimSpatial + 3>& output_strides,
-                             const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
-                             const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
-                             const std::array<ck::index_t, NDimSpatial>& input_left_pads,
-                             const std::array<ck::index_t, NDimSpatial>& input_right_pads,
-                             InElementwiseOperation in_element_op,
-                             WeiElementwiseOperation wei_element_op,
-                             OutElementwiseOperation out_element_op,
-                             const ck::index_t split_k)
+    static auto
+    MakeArgument(const InDataType* p_in_grid,
+                 WeiDataType* p_wei_grid,
+                 const OutDataType* p_out_grid,
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
+                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
+                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
+                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
+                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+                 const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+                 const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op,
+                 const ck::index_t split_k)
     {
         return Argument{p_in_grid,
                         p_wei_grid,
                         p_out_grid,
-                        G,
-                        N,
-                        K,
-                        C,
-                        input_spatial_lengths,
-                        filter_spatial_lengths,
-                        output_spatial_lengths,
-                        input_strides,
-                        output_strides,
+                        a_g_n_c_wis_lengths, // input
+                        a_g_n_c_wis_strides,
+                        b_g_k_c_xs_lengths, // weight
+                        b_g_k_c_xs_strides,
+                        e_g_n_k_wos_lengths, // output
+                        e_g_n_k_wos_strides,
                         conv_filter_strides,
                         conv_filter_dilations,
                         input_left_pads,
@@ -1390,15 +1449,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
     MakeArgumentPointer(const void* p_in_grid,
                         void* p_wei_grid,
                         const void* p_out_grid,
-                        const ck::index_t G,
-                        const ck::index_t N,
-                        const ck::index_t K,
-                        const ck::index_t C,
-                        const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-                        const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-                        const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-                        const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
-                        const std::array<ck::index_t, NDimSpatial + 3>& output_strides,
+                        const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
+                        const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
+                        const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
+                        const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
+                        const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
+                        const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
                         const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
                         const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
                         const std::array<ck::index_t, NDimSpatial>& input_left_pads,
@@ -1411,15 +1467,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
                                           static_cast<WeiDataType*>(p_wei_grid),
                                           static_cast<const OutDataType*>(p_out_grid),
-                                          G,
-                                          N,
-                                          K,
-                                          C,
-                                          input_spatial_lengths,
-                                          filter_spatial_lengths,
-                                          output_spatial_lengths,
-                                          input_strides,
-                                          output_strides,
+                                          a_g_n_c_wis_lengths, // input
+                                          a_g_n_c_wis_strides,
+                                          b_g_k_c_xs_lengths, // weight
+                                          b_g_k_c_xs_strides,
+                                          e_g_n_k_wos_lengths, // output
+                                          e_g_n_k_wos_strides,
                                           conv_filter_strides,
                                           conv_filter_dilations,
                                           input_left_pads,

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v1.hpp

@@ -35,13 +35,17 @@ __global__ void
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
 
-template <typename GridwiseGemm, typename FloatAB, typename FloatC, bool HasMainKBlockLoop>
+template <typename GridwiseGemm,
+          typename FloatA,
+          typename FloatB,
+          typename FloatC,
+          bool HasMainKBlockLoop>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
     __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
 #endif
-        kernel_gemm_xdl_cshuffle_v1(const FloatAB* __restrict__ p_a_grid,
-                                    const FloatAB* __restrict__ p_b_grid,
+        kernel_gemm_xdl_cshuffle_v1(const FloatA* __restrict__ p_a_grid,
+                                    const FloatB* __restrict__ p_b_grid,
                                     FloatC* __restrict__ p_c_grid,
                                     typename GridwiseGemm::Problem problem)
 {
@@ -61,7 +65,8 @@ __global__ void
 template <typename ALayout,
           typename BLayout,
           typename CLayout,
-          typename FloatAB,
+          typename FloatA,
+          typename FloatB,
           typename FloatGemmAcc,
           typename FloatCShuffle,
           typename FloatC,
@@ -102,7 +107,8 @@ template <typename ALayout,
           typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
           LoopScheduler LoopSched,
-          PipelineVersion PipelineVer = PipelineVersion::v1>
+          PipelineVersion PipelineVer = PipelineVersion::v1,
+          typename ComputeType        = FloatC>
 struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
 {
     static constexpr auto I0 = Number<0>{};
@@ -463,8 +469,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
     // Argument
     struct Argument : public tensor_operation::device::BaseArgument, public Problem
     {
-        __host__ Argument(const FloatAB* p_a_grid_,
-                          const FloatAB* p_b_grid_,
+        __host__ Argument(const FloatA* p_a_grid_,
+                          const FloatB* p_b_grid_,
                           FloatC* p_c_grid_,
                           index_t M_,
                           index_t N_,
@@ -479,8 +485,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
         {
         }
 
-        const FloatAB* p_a_grid;
-        const FloatAB* p_b_grid;
+        const FloatA* p_a_grid;
+        const FloatB* p_b_grid;
         FloatC* p_c_grid;
     };
 
@@ -541,8 +547,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
         constexpr auto c_block_size =
             c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
 
-        return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
-                             sizeof(FloatAB),
+        return math::max((a_block_space_size_aligned * sizeof(ComputeType) +
+                          b_block_space_size_aligned * sizeof(ComputeType)),
                          c_block_size * sizeof(FloatCShuffle));
     }
 
@@ -676,8 +682,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
     using Block2CTileMap = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
 
     template <bool HasMainKBlockLoop>
-    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
-                               const FloatAB* __restrict__ p_b_grid,
+    __device__ static void Run(const FloatA* __restrict__ p_a_grid,
+                               const FloatB* __restrict__ p_b_grid,
                                FloatC* __restrict__ p_c_grid,
                                void* __restrict__ p_shared,
                                const Problem& problem)
@@ -743,8 +749,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
                                                 Sequence<AK0Number, MPerBlock, AK1Number>,
                                                 ABlockTransferThreadClusterLengths_AK0_M_AK1,
                                                 ABlockTransferThreadClusterArrangeOrder,
-                                                FloatAB,
-                                                FloatAB,
+                                                FloatA,
+                                                ComputeType,
                                                 decltype(a_grid_desc_ak0_m_ak1),
                                                 decltype(a_block_desc_ak0_m_ak1),
                                                 ABlockTransferSrcAccessOrder,
@@ -774,8 +780,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
                                                 Sequence<BK0Number, NPerBlock, BK1Number>,
                                                 BBlockTransferThreadClusterLengths_BK0_N_BK1,
                                                 BBlockTransferThreadClusterArrangeOrder,
-                                                FloatAB,
-                                                FloatAB,
+                                                FloatB,
+                                                ComputeType,
                                                 decltype(b_grid_desc_bk0_n_bk1),
                                                 decltype(b_block_desc_bk0_n_bk1),
                                                 BBlockTransferSrcAccessOrder,
@@ -805,11 +811,11 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
         // sanity check
         constexpr index_t KPack =
             math::max(math::lcm(AK1Number, BK1Number),
-                      MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
+                      MfmaSelector<ComputeType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
 
         auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
             BlockSize,
-            FloatAB,
+            ComputeType,
             FloatGemmAcc,
             decltype(a_block_desc_ak0_m_ak1),
             decltype(b_block_desc_bk0_n_bk1),
@@ -827,10 +833,10 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
             a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
 
         auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<FloatAB*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+            static_cast<ComputeType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
 
         auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<FloatAB*>(p_shared) + a_block_space_size_aligned,
+            static_cast<ComputeType*>(p_shared) + a_block_space_size_aligned,
             b_block_desc_bk0_n_bk1.GetElementSpaceSize());
 
         constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);

include/ck/tensor_operation/gpu/grid/gridwise_put_element_1d.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+#include "ck/library/utility/host_tensor.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace host {
+
+// dinput descriptor in [N, C, Do, Ho, Wo] order
+// doutput descriptor in [N, C, Di, Hi, Wi] order
+// phyiscal layout is irrelavent
+template <ck::index_t NDimSpatial,
+          typename DInDataType,
+          typename DOutDataType,
+          typename std::enable_if<NDimSpatial >= 1 && NDimSpatial <= 3, bool>::type = false>
+struct ReferenceAvgPoolBwd : public device::BaseOperator
+{
+    // Argument
+    struct Argument : public device::BaseArgument
+    {
+        Argument(Tensor<DInDataType>& dinput,
+                 const Tensor<DOutDataType>& doutput,
+                 std::vector<ck::index_t> window_spatial_lengths,
+                 std::vector<ck::index_t> window_strides,
+                 std::vector<ck::index_t> window_dilations,
+                 std::vector<ck::index_t> dinput_left_pads,
+                 std::vector<ck::index_t> dinput_right_pads)
+            : dinput_{dinput},
+              doutput_{doutput},
+              window_spatial_lengths_{window_spatial_lengths},
+              window_strides_{window_strides},
+              window_dilations_{window_dilations},
+              in_left_pads_{dinput_left_pads},
+              in_right_pads_{dinput_right_pads}
+        {
+        }
+
+        Tensor<DInDataType>& dinput_;
+        const Tensor<DOutDataType>& doutput_;
+
+        std::vector<ck::index_t> window_spatial_lengths_;
+        std::vector<index_t> window_strides_;
+        std::vector<index_t> window_dilations_;
+        std::vector<index_t> in_left_pads_;
+        std::vector<index_t> in_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public device::BaseInvoker
+    {
+        using Argument = ReferenceAvgPoolBwd::Argument;
+
+        template <ck::index_t NDimSpatial_,
+                  typename std::enable_if<NDimSpatial_ == 1, bool>::type = false>
+        float RunAvgPoolBwd(const Argument& arg)
+        {
+            // Let input = x, outpu = y
+            // shape of x = [10], y = [6]
+            // window_size = 5, pad = 0, stride = 1, dilation = 1
+            // Forward:
+            // y0 = 1/5 * (x0 + x1 + x2 + x3 + x4)
+            // y1 = 1/5 * (x1 + x2 + x3 + x4 + x5)
+            // ...
+            // y5 = 1/5 * (x5 + x6 + x7 + x8 + x9)
+            // y6 = 1/5 * (x6 + x7 + x8 + x9)
+            // ...
+            // y9 = 1/5 * (x9)
+
+            // Backward:
+            // shape of dy = [6], dx = [10]
+            // dx0 = 1/5 * dy0
+            // dx1 = 1/5 * (dy0 + dy1)
+            // dx2 = 1/5 * (dy0 + dy1 + dy2)
+            // ...
+            // dx4 = 1/5 * (dy0 + dy1 + dy2 + dy3 + dy4)
+            // dx5 = 1/5 * (dy1 + dy2 + dy3 + dy4 + dy5)
+            // ...
+            // dx9 = 1/5 * (dy5 + dy6 + dy7 + dy8 + dy9)
+
+            auto f_ncw = [&](auto n, auto c, auto wi) {
+                std::size_t X  = arg.window_spatial_lengths_[0];
+                std::size_t Wo = arg.doutput_.GetLengths()[2];
+
+                float v_acc = 0;
+
+                for(std::size_t x = 0; x < X; ++x)
+                {
+                    // Out_Position = (In_Position + pad - x * dilation) / stride
+                    auto w_tmp = static_cast<ck::long_index_t>(wi) +
+                                 static_cast<ck::long_index_t>(arg.in_left_pads_[0]) -
+                                 static_cast<ck::long_index_t>(x * arg.window_dilations_[0]);
+
+                    // Check the input pixel validity (in perspective of being affected by some
+                    // doutput pixel)
+                    if(w_tmp % arg.window_strides_[0] == 0)
+                    {
+                        auto wo = static_cast<ck::long_index_t>(w_tmp) /
+                                  static_cast<ck::long_index_t>(arg.window_strides_[0]);
+
+                        // Get the doutput pixel in valid range to accumulate the gradients for this
+                        // input pixel
+                        if(wo >= 0 && ck::type_convert<std::size_t>(wo) < Wo)
+                        {
+                            v_acc += ck::type_convert<float>(arg.doutput_(n, c, wo));
+                        }
+                    }
+                }
+
+                v_acc /= ck::type_convert<float>(X);
+                arg.dinput_(n, c, wi) = ck::type_convert<DInDataType>(v_acc);
+            };
+
+            make_ParallelTensorFunctor(f_ncw,
+                                       arg.dinput_.GetLengths()[0],
+                                       arg.dinput_.GetLengths()[1],
+                                       arg.dinput_.GetLengths()[2])(
+                std::thread::hardware_concurrency());
+
+            return 0;
+        }
+
+        template <ck::index_t NDimSpatial_,
+                  typename std::enable_if<NDimSpatial_ == 2, bool>::type = false>
+        float RunAvgPoolBwd(const Argument& arg)
+        {
+            auto f_nchw = [&](auto n, auto c, auto hi, auto wi) {
+                std::size_t Y = arg.window_spatial_lengths_[0];
+                std::size_t X = arg.window_spatial_lengths_[1];
+
+                std::size_t Ho = arg.doutput_.GetLengths()[2];
+                std::size_t Wo = arg.doutput_.GetLengths()[3];
+
+                float v_acc = 0;
+
+                for(std::size_t y = 0; y < Y; ++y)
+                {
+                    // Out_Position = (In_Position + pad - x * dilation) / stride
+                    auto h_tmp = static_cast<ck::long_index_t>(hi) +
+                                 static_cast<ck::long_index_t>(arg.in_left_pads_[0]) -
+                                 static_cast<ck::long_index_t>(y * arg.window_dilations_[0]);
+
+                    // Check the input pixel validity (in perspective of being affected by some
+                    // doutput pixel)
+                    if(h_tmp % arg.window_strides_[0] == 0)
+                    {
+                        auto ho = static_cast<ck::long_index_t>(h_tmp) /
+                                  static_cast<ck::long_index_t>(arg.window_strides_[0]);
+
+                        // Get the doutput pixel in valid range to accumulate the gradients for this
+                        // input pixel
+                        if(ho >= 0 && ck::type_convert<std::size_t>(ho) < Ho)
+                        {
+                            for(std::size_t x = 0; x < X; ++x)
+                            {
+                                auto w_tmp =
+                                    static_cast<ck::long_index_t>(wi) +
+                                    static_cast<ck::long_index_t>(arg.in_left_pads_[1]) -
+                                    static_cast<ck::long_index_t>(x * arg.window_dilations_[1]);
+                                if(w_tmp % arg.window_strides_[1] == 0)
+                                {
+                                    auto wo = static_cast<ck::long_index_t>(w_tmp) /
+                                              static_cast<ck::long_index_t>(arg.window_strides_[1]);
+                                    if(wo >= 0 && ck::type_convert<std::size_t>(wo) < Wo)
+                                    {
+                                        v_acc +=
+                                            ck::type_convert<float>(arg.doutput_(n, c, ho, wo));
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+
+                v_acc /= ck::type_convert<float>(Y * X);
+                arg.dinput_(n, c, hi, wi) = ck::type_convert<DInDataType>(v_acc);
+            };
+
+            make_ParallelTensorFunctor(f_nchw,
+                                       arg.dinput_.GetLengths()[0],
+                                       arg.dinput_.GetLengths()[1],
+                                       arg.dinput_.GetLengths()[2],
+                                       arg.dinput_.GetLengths()[3])(
+                std::thread::hardware_concurrency());
+
+            return 0;
+        }
+
+        template <ck::index_t NDimSpatial_,
+                  typename std::enable_if<NDimSpatial_ == 3, bool>::type = false>
+        float RunAvgPoolBwd(const Argument& arg)
+        {
+            auto f_ncdhw = [&](auto n, auto c, auto di, auto hi, auto wi) {
+                std::size_t Z = arg.window_spatial_lengths_[0];
+                std::size_t Y = arg.window_spatial_lengths_[1];
+                std::size_t X = arg.window_spatial_lengths_[2];
+
+                std::size_t Do = arg.doutput_.GetLengths()[2];
+                std::size_t Ho = arg.doutput_.GetLengths()[3];
+                std::size_t Wo = arg.doutput_.GetLengths()[4];
+
+                float v_acc = 0;
+
+                for(std::size_t z = 0; z < Z; ++z)
+                {
+                    // Out_Position = (In_Position + pad - x * dilation) / stride
+                    auto d_tmp = static_cast<ck::long_index_t>(di) +
+                                 static_cast<ck::long_index_t>(arg.in_left_pads_[0]) -
+                                 static_cast<ck::long_index_t>(z * arg.window_dilations_[0]);
+
+                    // Check the input pixel validity (in perspective of being affected by some
+                    // doutput pixel)
+                    if(d_tmp % arg.window_strides_[0] == 0)
+                    {
+                        auto do_ = static_cast<ck::long_index_t>(d_tmp) /
+                                   static_cast<ck::long_index_t>(arg.window_strides_[0]);
+
+                        // Get the doutput pixel in valid range to accumulate the gradients for this
+                        // input pixel
+                        if(do_ >= 0 && ck::type_convert<std::size_t>(do_) < Do)
+                        {
+                            for(std::size_t y = 0; y < Y; ++y)
+                            {
+                                auto h_tmp =
+                                    static_cast<ck::long_index_t>(hi) +
+                                    static_cast<ck::long_index_t>(arg.in_left_pads_[1]) -
+                                    static_cast<ck::long_index_t>(y * arg.window_dilations_[1]);
+                                if(h_tmp % arg.window_strides_[1] == 0)
+                                {
+                                    auto ho = static_cast<ck::long_index_t>(h_tmp) /
+                                              static_cast<ck::long_index_t>(arg.window_strides_[1]);
+                                    if(ho >= 0 && ck::type_convert<std::size_t>(ho) < Ho)
+                                    {
+                                        for(std::size_t x = 0; x < X; ++x)
+                                        {
+                                            auto w_tmp = static_cast<ck::long_index_t>(wi) +
+                                                         static_cast<ck::long_index_t>(
+                                                             arg.in_left_pads_[2]) -
+                                                         static_cast<ck::long_index_t>(
+                                                             x * arg.window_dilations_[2]);
+
+                                            if(w_tmp % arg.window_strides_[2] == 0)
+                                            {
+                                                auto wo = static_cast<ck::long_index_t>(w_tmp) /
+                                                          static_cast<ck::long_index_t>(
+                                                              arg.window_strides_[2]);
+                                                if(wo >= 0 &&
+                                                   ck::type_convert<std::size_t>(wo) < Wo)
+                                                {
+                                                    v_acc += ck::type_convert<float>(
+                                                        arg.doutput_(n, c, do_, ho, wo));
+                                                }
+                                            }
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+
+                v_acc /= ck::type_convert<float>(Z * Y * X);
+                arg.dinput_(n, c, di, hi, wi) = ck::type_convert<DInDataType>(v_acc);
+            };
+
+            make_ParallelTensorFunctor(f_ncdhw,
+                                       arg.dinput_.GetLengths()[0],
+                                       arg.dinput_.GetLengths()[1],
+                                       arg.dinput_.GetLengths()[2],
+                                       arg.dinput_.GetLengths()[3],
+                                       arg.dinput_.GetLengths()[4])(
+                std::thread::hardware_concurrency());
+
+            return 0;
+        }
+
+        float Run(const Argument& arg)
+        {
+            if(!(arg.dinput_.GetNumOfDimension() == NDimSpatial + 2 &&
+                 arg.doutput_.GetNumOfDimension() == NDimSpatial + 2))
+            {
+                throw std::runtime_error("wrong! inconsistent dimension");
+            }
+
+            return RunAvgPoolBwd<NDimSpatial>(arg);
+        }
+
+        float Run(const device::BaseArgument* p_arg,
+                  const StreamConfig& /* stream_config */ = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg));
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
+
+    static auto MakeArgument(Tensor<DInDataType>& dinput,
+                             const Tensor<DOutDataType>& doutput,
+                             std::vector<ck::index_t> window_spatial_lengths,
+                             std::vector<ck::index_t> window_strides,
+                             std::vector<ck::index_t> window_dilations,
+                             std::vector<ck::index_t> dinput_left_pads,
+                             std::vector<ck::index_t> dinput_right_pads)
+    {
+        if(window_spatial_lengths.size() != NDimSpatial || window_strides.size() != NDimSpatial ||
+           window_dilations.size() != NDimSpatial || dinput_left_pads.size() != NDimSpatial ||
+           dinput_right_pads.size() != NDimSpatial)
+            throw std::runtime_error("dimension is incorrect");
+
+        return Argument{dinput,
+                        doutput,
+                        window_spatial_lengths,
+                        window_strides,
+                        window_dilations,
+                        dinput_left_pads,
+                        dinput_right_pads};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "ReferenceAvgPoolBwd"
+            << std::endl;
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace host
+} // namespace tensor_operation
+} // namespace ck

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

@@ -125,7 +125,7 @@ struct ReferenceConvBwdData : public device::BaseOperator
 
                     arg.in_element_op_(v_in, v_acc);
 
-                    arg.input_(g, n, c, wi) = ck::type_convert<InDataType>(v_acc);
+                    arg.input_(g, n, c, wi) = ck::type_convert<InDataType>(v_in);
                 };
 
                 make_ParallelTensorFunctor(f_ncw,
@@ -201,7 +201,7 @@ struct ReferenceConvBwdData : public device::BaseOperator
 
                     arg.in_element_op_(v_in, v_acc);
 
-                    arg.input_(g, n, c, hi, wi) = ck::type_convert<InDataType>(v_acc);
+                    arg.input_(g, n, c, hi, wi) = ck::type_convert<InDataType>(v_in);
                 };
 
                 make_ParallelTensorFunctor(f_nchw,
@@ -299,7 +299,7 @@ struct ReferenceConvBwdData : public device::BaseOperator
 
                     arg.in_element_op_(v_in, v_acc);
 
-                    arg.input_(g, n, c, di, hi, wi) = ck::type_convert<InDataType>(v_acc);
+                    arg.input_(g, n, c, di, hi, wi) = ck::type_convert<InDataType>(v_in);
                 };
 
                 make_ParallelTensorFunctor(f_ncdhw,

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm.hpp

@@ -16,7 +16,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 namespace instance {
-
+#ifdef __bf16__
 void add_device_batched_gemm_xdl_bf16_bf16_bf16_gkm_gkn_gmn_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemm<Col, Row, Row, BF16, BF16, BF16, PassThrough, PassThrough, PassThrough>>>&
@@ -36,7 +36,8 @@ void add_device_batched_gemm_xdl_bf16_bf16_bf16_gmk_gnk_gmn_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemm<Row, Col, Row, BF16, BF16, BF16, PassThrough, PassThrough, PassThrough>>>&
         instances);
-
+#endif
+#ifdef __fp16__
 void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
@@ -56,7 +57,8 @@ void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
         instances);
-
+#endif
+#ifdef __fp32__
 void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemm<Col, Row, Row, F32, F32, F32, PassThrough, PassThrough, PassThrough>>>&
@@ -76,7 +78,8 @@ void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemm<Row, Col, Row, F32, F32, F32, PassThrough, PassThrough, PassThrough>>>&
         instances);
-
+#endif
+#ifdef __int8__
 void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances(
     std::vector<std::unique_ptr<DeviceBatchedGemm<Col,
                                                   Row,
@@ -120,7 +123,7 @@ void add_device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instances(
                                                   PassThrough,
                                                   PassThrough,
                                                   PassThrough>>>& instances);
-
+#endif
 template <typename ALayout,
           typename BLayout,
           typename CLayout,
@@ -151,7 +154,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
     static auto GetInstances()
     {
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
-
+#ifdef __fp32__
         if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
                      is_same_v<CDataType, float>)
         {
@@ -176,8 +179,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
                 add_device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances(op_ptrs);
             }
         }
-        else if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
-                          is_same_v<CDataType, half_t>)
+#endif
+#ifdef __fp16__
+        if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
+                     is_same_v<CDataType, half_t>)
         {
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
@@ -200,8 +205,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
                 add_device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances(op_ptrs);
             }
         }
-        else if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, bhalf_t> &&
-                          is_same_v<CDataType, bhalf_t>)
+#endif
+#ifdef __bf16__
+        if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, bhalf_t> &&
+                     is_same_v<CDataType, bhalf_t>)
         {
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
@@ -224,8 +231,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
                 add_device_batched_gemm_xdl_bf16_bf16_bf16_gkm_gnk_gmn_instances(op_ptrs);
             }
         }
-        else if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
-                          is_same_v<CDataType, int8_t>)
+#endif
+#ifdef __int8__
+        if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
+                     is_same_v<CDataType, int8_t>)
         {
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
@@ -248,7 +257,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
                 add_device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instances(op_ptrs);
             }
         }
-
+#endif
         return op_ptrs;
     }
 };

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_add_relu_gemm_add.hpp

@@ -14,7 +14,7 @@
 
 using CDE0ElementOp = ck::tensor_operation::element_wise::AddRelu;
 using CDE1ElementOp = ck::tensor_operation::element_wise::Add;
-
+#ifdef __fp16__
 namespace ck {
 namespace tensor_operation {
 namespace device {
@@ -137,3 +137,4 @@ struct DeviceOperationInstanceFactory<
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
+#endif

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_bias_permute.hpp

@@ -13,7 +13,7 @@
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 #include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
-
+#ifdef __fp16__
 namespace ck {
 namespace tensor_operation {
 namespace device {
@@ -91,3 +91,4 @@ struct DeviceOperationInstanceFactory<
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
+#endif

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_bias_softmax_gemm_permute.hpp

@@ -16,7 +16,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 namespace instance {
-
+#ifdef __fp16__
 void add_device_batched_gemm_bias_masking_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_f16_gmk_gnk_gno_gmo_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemmSoftmaxGemmPermute<2,
@@ -58,7 +58,8 @@ void add_device_batched_gemm_bias_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_
                                                             PassThrough,
                                                             MaskingSpecialization::MaskDisabled>>>&
         instances);
-
+#endif
+#ifdef __bf16__
 void add_device_batched_gemm_bias_masking_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemmSoftmaxGemmPermute<2,
@@ -100,7 +101,7 @@ void add_device_batched_gemm_bias_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf
                                                             PassThrough,
                                                             MaskingSpecialization::MaskDisabled>>>&
         instances);
-
+#endif
 template <typename ADataType,
           typename B0DataType,
           typename B1DataType,
@@ -147,7 +148,7 @@ struct DeviceOperationInstanceFactory<
     static auto GetInstances()
     {
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
-
+#ifdef __fp16__
         if constexpr(is_same_v<ADataType, half_t> && is_same_v<B0DataType, half_t> &&
                      is_same_v<B1DataType, half_t> && is_same_v<CDataType, half_t> &&
                      Acc0BiasDataType::Size() == 1 &&
@@ -164,6 +165,8 @@ struct DeviceOperationInstanceFactory<
                     op_ptrs);
             }
         }
+#endif
+#ifdef __bf16__
         else if constexpr(is_same_v<ADataType, BF16> && is_same_v<B0DataType, BF16> &&
                           is_same_v<B1DataType, BF16> && is_same_v<CDataType, BF16> &&
                           Acc0BiasDataType::Size() == 1 &&
@@ -180,6 +183,7 @@ struct DeviceOperationInstanceFactory<
                     op_ptrs);
             }
         }
+#endif
         return op_ptrs;
     }
 };

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_gemm.hpp

@@ -16,7 +16,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 namespace instance {
-
+#ifdef __fp16__
 void add_device_batched_gemm_gemm_xdl_cshuffle_f16_f16_f16_f16_gmk_gnk_gno_gmo_instance(
     std::vector<std::unique_ptr<DeviceBatchedGemmGemm<Row,
                                                       Col,
@@ -111,3 +111,4 @@ struct DeviceOperationInstanceFactory<
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
+#endif

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_multi_d.hpp

@@ -19,7 +19,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 namespace instance {
-
+#ifdef __fp16__
 void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gkm_gkn_gmn_instances(
     std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
                                                         Row,
@@ -123,7 +123,8 @@ void add_device_batched_gemm_multi_d_dl_f16_f16_f16_gmk_gnk_gmn_irregular_instan
                                                         PassThrough,
                                                         PassThrough,
                                                         PassThrough>>>& instances);
-
+#endif
+#ifdef __int8__
 void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gkm_gkn_gmn_instances(
     std::vector<std::unique_ptr<DeviceBatchedGemmMultiD<Col,
                                                         Row,
@@ -227,7 +228,7 @@ void add_device_batched_gemm_multi_d_dl_i8_i8_i8_gmk_gnk_gmn_irregular_instances
                                                         PassThrough,
                                                         PassThrough,
                                                         PassThrough>>>& instances);
-
+#endif
 template <typename ALayout,
           typename BLayout,
           typename ELayout,
@@ -262,7 +263,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
     static auto GetInstances()
     {
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
-
+#ifdef __fp16__
         if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
                      is_same_v<EDataType, half_t>)
         {
@@ -295,6 +296,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
                     op_ptrs);
             }
         }
+#endif
+#ifdef __int8__
         else if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
                           is_same_v<EDataType, int8_t>)
         {
@@ -327,7 +330,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceBatche
                     op_ptrs);
             }
         }
-
+#endif
         return op_ptrs;
     }
 };

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_softmax_gemm.hpp

@@ -11,7 +11,7 @@
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 #include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
-
+#ifdef __fp16__
 namespace ck {
 namespace tensor_operation {
 namespace device {
@@ -119,3 +119,4 @@ struct DeviceOperationInstanceFactory<
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck
+#endif

library/include/ck/library/tensor_operation_instance/gpu/batched_gemm_softmax_gemm_permute.hpp

@@ -16,7 +16,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 namespace instance {
-
+#ifdef __fp16__
 void add_device_batched_gemm_masking_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_f16_gmk_gnk_gno_gmo_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemmSoftmaxGemmPermute<2,
@@ -58,7 +58,8 @@ void add_device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_f16_f16_f16_f16_g
                                                             PassThrough,
                                                             MaskingSpecialization::MaskDisabled>>>&
         instances);
-
+#endif
+#ifdef __bf16__
 void add_device_batched_gemm_masking_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instances(
     std::vector<std::unique_ptr<
         DeviceBatchedGemmSoftmaxGemmPermute<2,
@@ -100,6 +101,7 @@ void add_device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf
                                                             PassThrough,
                                                             MaskingSpecialization::MaskDisabled>>>&
         instances);
+#endif
 
 template <typename ADataType,
           typename B0DataType,
@@ -146,7 +148,7 @@ struct DeviceOperationInstanceFactory<
     static auto GetInstances()
     {
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
-
+#ifdef __fp16__
         if constexpr(is_same_v<ADataType, half_t> && is_same_v<B0DataType, half_t> &&
                      is_same_v<B1DataType, half_t> && is_same_v<CDataType, half_t>)
         {
@@ -161,6 +163,8 @@ struct DeviceOperationInstanceFactory<
                     op_ptrs);
             }
         }
+#endif
+#ifdef __bf16__
         else if constexpr(is_same_v<ADataType, BF16> && is_same_v<B0DataType, BF16> &&
                           is_same_v<B1DataType, BF16> && is_same_v<CDataType, BF16>)
         {
@@ -175,6 +179,7 @@ struct DeviceOperationInstanceFactory<
                     op_ptrs);
             }
         }
+#endif
         return op_ptrs;
     }
 };

library/include/ck/library/tensor_operation_instance/gpu/contraction_bilinear.hpp

@@ -16,7 +16,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 namespace instance {
-
+#ifdef __fp32__
 // float
 void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn_instance(
     std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
@@ -65,7 +65,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn
                                                            PassThrough,
                                                            PassThrough,
                                                            Bilinear>>>& instances);
-
+#endif
+#ifdef __fp64__
 // double
 void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn_instance(
     std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
@@ -114,7 +115,7 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mnnn
                                                            PassThrough,
                                                            PassThrough,
                                                            Bilinear>>>& instances);
-
+#endif
 // Contraction + Bilinear
 template <index_t NumDimM,
           index_t NumDimN,
@@ -149,7 +150,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
     static auto GetInstances()
     {
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
-
+#ifdef __fp32__
         if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
                      is_same_v<DDataType, float> && is_same_v<EDataType, float>)
         {
@@ -165,7 +166,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
                     op_ptrs);
             }
         }
-
+#endif
+#ifdef __fp64__
         if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> &&
                      is_same_v<DDataType, double> && is_same_v<EDataType, double>)
         {
@@ -181,7 +183,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
                     op_ptrs);
             }
         }
-
+#endif
         return op_ptrs;
     }
 };

library/include/ck/library/tensor_operation_instance/gpu/contraction_scale.hpp

@@ -16,7 +16,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 namespace instance {
-
+#ifdef __fp32__
 // float
 void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instance(
     std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
@@ -65,7 +65,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instanc
                                                            PassThrough,
                                                            PassThrough,
                                                            Scale>>>& instances);
-
+#endif
+#ifdef __fp64__
 // double
 void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instance(
     std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
@@ -114,7 +115,7 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mnn_instanc
                                                            PassThrough,
                                                            PassThrough,
                                                            Scale>>>& instances);
-
+#endif
 // Contraction + Scale
 template <index_t NumDimM,
           index_t NumDimN,
@@ -148,7 +149,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
     static auto GetInstances()
     {
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
-
+#ifdef __fp32__
         if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
                      is_same_v<EDataType, float>)
         {
@@ -164,7 +165,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
                     op_ptrs);
             }
         }
-
+#endif
+#ifdef __fp64__
         if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> &&
                      is_same_v<EDataType, double>)
         {
@@ -180,7 +182,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
                     op_ptrs);
             }
         }
-
+#endif
         return op_ptrs;
     }
 };