Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/composable_kernel into lwpck-977

example/62_conv_fwd_activ/convnd_fwd_xdl_pow_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "convnd_fwd_activ_common.hpp"
+
+using OutElementOp = ck::tensor_operation::element_wise::Power;
+
+using DeviceGroupedConvNDFwdActivInstance = DeviceGroupedConvNDFwdInstance<OutElementOp>;
+#include "run_convnd_fwd_activ_example.inc"
+
+int main(int argc, char* argv[]) { return !run_convnd_fwd_example(argc, argv); }

example/62_conv_fwd_activ/convnd_fwd_xdl_relu_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "convnd_fwd_activ_common.hpp"
+
+using OutElementOp = ck::tensor_operation::element_wise::Relu;
+
+using DeviceGroupedConvNDFwdActivInstance = DeviceGroupedConvNDFwdInstance<OutElementOp>;
+#include "run_convnd_fwd_activ_example.inc"
+
+int main(int argc, char* argv[]) { return !run_convnd_fwd_example(argc, argv); }

example/62_conv_fwd_activ/convnd_fwd_xdl_sigmoid_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "convnd_fwd_activ_common.hpp"
+
+using OutElementOp = ck::tensor_operation::element_wise::Sigmoid;
+
+using DeviceGroupedConvNDFwdActivInstance = DeviceGroupedConvNDFwdInstance<OutElementOp>;
+#include "run_convnd_fwd_activ_example.inc"
+
+int main(int argc, char* argv[]) { return !run_convnd_fwd_example(argc, argv); }

example/62_conv_fwd_activ/convnd_fwd_xdl_softrelu_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "convnd_fwd_activ_common.hpp"
+
+using OutElementOp = ck::tensor_operation::element_wise::SoftRelu;
+
+using DeviceGroupedConvNDFwdActivInstance = DeviceGroupedConvNDFwdInstance<OutElementOp>;
+#include "run_convnd_fwd_activ_example.inc"
+
+int main(int argc, char* argv[]) { return !run_convnd_fwd_example(argc, argv); }

example/62_conv_fwd_activ/convnd_fwd_xdl_tanh_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "convnd_fwd_activ_common.hpp"
+
+using OutElementOp = ck::tensor_operation::element_wise::TanH;
+
+using DeviceGroupedConvNDFwdActivInstance = DeviceGroupedConvNDFwdInstance<OutElementOp>;
+#include "run_convnd_fwd_activ_example.inc"
+
+int main(int argc, char* argv[]) { return !run_convnd_fwd_example(argc, argv); }

example/62_conv_fwd_activ/run_convnd_fwd_activ_example.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+void print_helper_msg()
+{
+    std::cout << "arg1: verification (0=no, 1=yes)\n"
+              << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
+              << "arg3: time kernel (0=no, 1=yes)\n"
+              << ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
+}
+
+bool run_convnd_fwd_example(int argc, char* argv[])
+{
+    print_helper_msg();
+
+    bool do_verification = true;
+    // Use floats for SoftRelu by default to avoid overflow after e^x.
+    int init_method =
+        std::is_same_v<OutElementOp, ck::tensor_operation::element_wise::SoftRelu> ? 2 : 1;
+    bool time_kernel = false;
+
+    // Following shapes are selected to avoid overflow. Expect inf in case of
+    // size increase for some elementwise ops.
+    ck::utils::conv::ConvParam conv_param{
+        3, 1, 16, 128, 8, {3, 3, 3}, {17, 17, 17}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}};
+
+    if(argc == 1)
+    {
+        // use default
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else
+    {
+        do_verification                   = std::stoi(argv[1]);
+        init_method                       = std::stoi(argv[2]);
+        time_kernel                       = std::stoi(argv[3]);
+        const ck::index_t num_dim_spatial = std::stoi(argv[4]);
+
+        conv_param = ck::utils::conv::parse_conv_param(num_dim_spatial, 5, argv);
+    }
+
+    const auto in_element_op  = InElementOp{};
+    const auto wei_element_op = WeiElementOp{};
+    const auto out_element_op = OutElementOp{};
+
+    const auto run = [&]() {
+        const auto in_g_n_c_wis_desc =
+            ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
+                conv_param);
+
+        const auto wei_g_k_c_xs_desc =
+            ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
+                conv_param);
+
+        const auto out_g_n_k_wos_desc =
+            ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
+                conv_param);
+
+        return run_grouped_conv_fwd<NDimSpatial,
+                                    InDataType,
+                                    WeiDataType,
+                                    OutDataType,
+                                    InElementOp,
+                                    WeiElementOp,
+                                    OutElementOp,
+                                    DeviceGroupedConvNDFwdActivInstance>(do_verification,
+                                                                         init_method,
+                                                                         time_kernel,
+                                                                         conv_param,
+                                                                         in_g_n_c_wis_desc,
+                                                                         wei_g_k_c_xs_desc,
+                                                                         out_g_n_k_wos_desc,
+                                                                         in_element_op,
+                                                                         wei_element_op,
+                                                                         out_element_op);
+    };
+
+    if(conv_param.num_dim_spatial_ == 3)
+    {
+        return run();
+    }
+
+    return false;
+}

example/CMakeLists.txt

@@ -62,6 +62,12 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
     set(result ${result} PARENT_SCOPE)
 endfunction(add_example_executable EXAMPLE_NAME)
 
+function(add_example_dependencies EXAMPLE_NAME FILE_NAME)
+    if(result EQUAL 0)
+        add_dependencies(${EXAMPLE_NAME} ${FILE_NAME})
+    endif()
+endfunction(add_example_dependencies EXAMPLE_NAME)
+
 function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
     message("adding example ${EXAMPLE_NAME}")
     set(result 1)

include/ck/ck.hpp

@@ -66,6 +66,10 @@
 #define CK_USE_AMD_V_FMAC_F32
 #define CK_USE_AMD_V_DOT2_F32_F16
 #define CK_USE_AMD_V_DOT4_I32_I8
+#elif defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__)
+#define CK_USE_AMD_V_FMAC_F32
+#define CK_USE_AMD_V_DOT2_F32_F16
+#define CK_USE_AMD_V_DOT4_I32_I8_GFX11
 #endif
 
 // MFMA instruction

include/ck/host_utility/hip_check_error.hpp

@@ -3,8 +3,10 @@
 
 #pragma once
 
+#include <sstream>
 #include <hip/hip_runtime.h>
 
+// To be removed, which really does not tell the location of failed HIP functional call
 inline void hip_check_error(hipError_t x)
 {
     if(x != hipSuccess)
@@ -15,3 +17,16 @@ inline void hip_check_error(hipError_t x)
         throw std::runtime_error(ss.str());
     }
 }
+
+#define HIP_CHECK_ERROR(retval_or_funcall)                                         \
+    do                                                                             \
+    {                                                                              \
+        hipError_t _tmpVal = retval_or_funcall;                                    \
+        if(_tmpVal != hipSuccess)                                                  \
+        {                                                                          \
+            std::ostringstream ostr;                                               \
+            ostr << "HIP Function Failed (" << __FILE__ << "," << __LINE__ << ") " \
+                 << hipGetErrorString(_tmpVal);                                    \
+            throw std::runtime_error(ostr.str());                                  \
+        }                                                                          \
+    } while(0)

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <array>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// GEMM:
+//   input : A0[M0, M1, ... K0, K1, ...], ...
+//   input : B0[N0, N1, ... K0, K1, ...], ...
+//   input : D0[M0, M1, ... N0, N1, ...], D1[M0, M1, ... N0, N1, ...], ...
+//   output : E[M0, M1, ... N0, N1, ...]
+//   C = a_op(A) * b_op(B)
+//   E = cde_op(C, D0, D1, ...)
+// Assume:
+//   D0, D1, ... and E have the same layout
+template <index_t NumDimM,
+          index_t NumDimN,
+          index_t NumDimK,
+          typename AsDataType,
+          typename BsDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation>
+struct DeviceContractionMultipleABD : public BaseOperator
+{
+    static constexpr index_t NumATensor = AsDataType::Size();
+    static constexpr index_t NumBTensor = BsDataType::Size();
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(std::array<const void*, NumATensor> p_as,
+                        std::array<const void*, NumBTensor> p_bs,
+                        std::array<const void*, NumDTensor> p_ds,
+                        void* p_e,
+                        const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_lengths,
+                        const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_strides,
+                        const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_lengths,
+                        const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_strides,
+                        const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_lengths,
+                        const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_strides,
+                        const std::vector<index_t>& e_ms_ns_length,
+                        const std::vector<index_t>& e_ms_ns_stride,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation cde_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

@@ -14,8 +14,8 @@ namespace device {
 template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
-          typename ComputeDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename YElementwiseOperation,
           index_t Rank,
           index_t NumReduceDim>
@@ -27,6 +27,8 @@ struct DeviceNormalization : public BaseOperator
                         const std::vector<index_t> gammaStrides,
                         const std::vector<index_t> betaStrides,
                         const std::vector<index_t> yStrides,
+                        const std::vector<index_t> saveMeanStrides,
+                        const std::vector<index_t> saveInvStdStrides,
                         const std::vector<index_t> reduceDims,
                         double epsilon,
                         const void* p_x,
@@ -43,16 +45,16 @@ struct DeviceNormalization : public BaseOperator
 template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
-          typename ComputeDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename YElementwiseOperation,
           index_t Rank,
           index_t NumReduceDim>
 using DeviceNormalizationPtr = std::unique_ptr<DeviceNormalization<XDataType,
                                                                    GammaDataType,
                                                                    BetaDataType,
-                                                                   ComputeDataType,
                                                                    YDataType,
+                                                                   SaveMeanInvStdDataType,
                                                                    YElementwiseOperation,
                                                                    Rank,
                                                                    NumReduceDim>>;

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+#include <vector>
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_contraction_multiple_abd.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+
+template <typename GridwiseGemm,
+          typename AsPointer,
+          typename BsPointer,
+          typename DsPointer,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          typename AsGridDesc_AK0_M_AK1,
+          typename BsGridDesc_BK0_N_BK1,
+          typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename Block2ETileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_contraction_multiple_abd_xdl_cshuffle(
+            AsPointer p_as_grid,
+            BsPointer p_bs_grid,
+            DsPointer p_ds_grid,
+            EDataType* __restrict__ p_e_grid,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const CDEElementwiseOperation cde_element_op,
+            const AsGridDesc_AK0_M_AK1 as_grid_desc_ak0_m_ak1,
+            const BsGridDesc_BK0_N_BK1 bs_grid_desc_bk0_n_bk1,
+            const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                ds_grid_desc_mblock_mperblock_nblock_nperblock,
+            const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                e_grid_desc_mblock_mperblock_nblock_nperblock,
+            const Block2ETileMap block_2_etile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_as_grid,
+                                                  p_bs_grid,
+                                                  p_ds_grid,
+                                                  p_e_grid,
+                                                  p_shared,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  cde_element_op,
+                                                  as_grid_desc_ak0_m_ak1,
+                                                  bs_grid_desc_bk0_n_bk1,
+                                                  ds_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  e_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  block_2_etile_map);
+#else
+    ignore = p_as_grid;
+    ignore = p_bs_grid;
+    ignore = p_ds_grid;
+    ignore = p_e_grid;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = cde_element_op;
+    ignore = as_grid_desc_ak0_m_ak1;
+    ignore = bs_grid_desc_bk0_n_bk1;
+    ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = block_2_etile_map;
+#endif
+}
+
+} // namespace ck
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// GEMM:
+//   input : A[M, K]
+//   input : B[N, K]
+//   input : D0[M, N], D1[M, N], ...
+//   output : E[M, N]
+//   C = a_op(A) * b_op(B)
+//   E = cde_op(C, D0, D1, ...)
+// Assume:
+//   D0, D1, ... and E have the same layout
+template <index_t NumDimM,
+          index_t NumDimN,
+          index_t NumDimK,
+          typename AsDataType,
+          typename BsDataType,
+          typename AccDataType,
+          typename CShuffleDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          GemmSpecialization GemmSpec,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t AK1,
+          index_t BK1,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          index_t ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          index_t BBlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CDEBlockTransferScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched     = make_default_loop_scheduler(),
+          PipelineVersion PipelineVer = PipelineVersion::v1>
+struct DeviceContractionMultipleABD_Xdl_CShuffle
+    : public DeviceContractionMultipleABD<NumDimM,
+                                          NumDimN,
+                                          NumDimK,
+                                          AsDataType,
+                                          BsDataType,
+                                          DsDataType,
+                                          EDataType,
+                                          AElementwiseOperation,
+                                          BElementwiseOperation,
+                                          CDEElementwiseOperation>
+{
+    using DeviceOp = DeviceContractionMultipleABD_Xdl_CShuffle;
+
+    static constexpr index_t NumATensor = AsDataType::Size();
+    static constexpr index_t NumBTensor = BsDataType::Size();
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+
+    using ComputeDataType = EDataType;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemmMultipleABD_xdl_cshuffle<
+        AsDataType,
+        BsDataType,
+        ComputeDataType,
+        AccDataType,
+        CShuffleDataType,
+        DsDataType,
+        EDataType,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CDEElementwiseOperation,
+        InMemoryDataOperationEnum::Set,
+        NumGemmKPrefetchStage,
+        BlockSize,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        AK1,
+        BK1,
+        MPerXDL,
+        NPerXDL,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_AK0_M_AK1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_AK1,
+        false,
+        ABlockLdsExtraM,
+        BBlockTransferThreadClusterLengths_BK0_N_BK1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_BK1,
+        false,
+        BBlockLdsExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CDEBlockTransferScalarPerVector_NPerBlock,
+        LoopSched,
+        PipelineVer>;
+
+    static constexpr auto matrix_padder =
+        ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
+            MPerBlock, NPerBlock, KPerBlock};
+
+    static auto MakeAGridDescriptor_M_K(const std::vector<index_t>& a_ms_ks_lengths_,
+                                        const std::vector<index_t>& a_ms_ks_strides_)
+    {
+        assert(a_ms_ks_lengths_.size() == NumDimM + NumDimK &&
+               a_ms_ks_strides_.size() == NumDimM + NumDimK);
+
+        const auto to_tuple = [&](auto& vec, auto num) {
+            return generate_tuple([&](auto i) { return vec[i]; }, num);
+        };
+
+        const auto a_ms_ks_lengths = to_tuple(a_ms_ks_lengths_, Number<NumDimM + NumDimK>{});
+        const auto a_ms_ks_strides = to_tuple(a_ms_ks_strides_, Number<NumDimM + NumDimK>{});
+
+        // dimension Ids for M0, M1, ...
+        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
+
+        // dimension Ids for K0, K1, ...
+        constexpr auto kDimIds =
+            typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimK, 1>::type{};
+
+        // lengths for M0, M1, ...
+        const auto mLengths = get_container_subset(a_ms_ks_lengths, mDimIds);
+
+        // lengths for K0, K1, ...
+        const auto kLengths = get_container_subset(a_ms_ks_lengths, kDimIds);
+
+        // naive tensor A[M0, M1, M2, ..., K0, K1, K2...]
+        const auto a_grid_desc_ms_ks =
+            make_naive_tensor_descriptor(a_ms_ks_lengths, a_ms_ks_strides);
+
+        // transformed tensor A[MRaw = M0 * M1 * M2 * ... , KRaw = K0 * K1 * K2 * ...]
+        const auto a_grid_desc_mraw_kraw = transform_tensor_descriptor(
+            a_grid_desc_ms_ks,
+            make_tuple(make_merge_transform(mLengths), make_merge_transform(kLengths)),
+            make_tuple(mDimIds, kDimIds),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
+    }
+
+    __host__ __device__ static auto
+    MakeAsGridDescriptor_M_K(const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_lengths,
+                             const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_strides)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                return MakeAGridDescriptor_M_K(as_ms_ks_lengths[i], as_ms_ks_strides[i]);
+            },
+            Number<NumATensor>{});
+    }
+
+    // Assume: B[N0, N1, N2, ..., K0, K1, K2, ...]
+    static auto MakeBGridDescriptor_N_K(const std::vector<index_t>& b_ns_ks_lengths_,
+                                        const std::vector<index_t>& b_ns_ks_strides_)
+    {
+        assert(b_ns_ks_lengths_.size() == NumDimN + NumDimK &&
+               b_ns_ks_strides_.size() == NumDimN + NumDimK);
+
+        const auto to_tuple = [&](auto& vec, auto num) {
+            return generate_tuple([&](auto i) { return vec[i]; }, num);
+        };
+
+        const auto b_ns_ks_lengths = to_tuple(b_ns_ks_lengths_, Number<NumDimN + NumDimK>{});
+        const auto b_ns_ks_strides = to_tuple(b_ns_ks_strides_, Number<NumDimN + NumDimK>{});
+
+        // dimension Ids for N0, N1, ...
+        constexpr auto nDimIds = typename arithmetic_sequence_gen<0, NumDimN, 1>::type{};
+
+        // dimension Ids for K0, K1, ...
+        constexpr auto kDimIds =
+            typename arithmetic_sequence_gen<NumDimN, NumDimN + NumDimK, 1>::type{};
+
+        // lengths for K0, K1, ...
+        const auto kLengths = get_container_subset(b_ns_ks_lengths, kDimIds);
+
+        // lengths for N0, N1, ...
+        const auto nLengths = get_container_subset(b_ns_ks_lengths, nDimIds);
+
+        // naive tensor B[N0, N1, N2, ..., K0, K1, K2, ...]
+        const auto b_grid_desc_ns_ks =
+            make_naive_tensor_descriptor(b_ns_ks_lengths, b_ns_ks_strides);
+
+        // transformed tensor B[NRaw = N0 * N1 * N2 * ..., KRaw = K0 * K1 * K2 * ...]
+        const auto b_grid_desc_nraw_kraw = transform_tensor_descriptor(
+            b_grid_desc_ns_ks,
+            make_tuple(make_merge_transform(nLengths), make_merge_transform(kLengths)),
+            make_tuple(nDimIds, kDimIds),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
+    }
+
+    __host__ __device__ static auto
+    MakeBsGridDescriptor_N_K(const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_lengths,
+                             const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_strides)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                return MakeBGridDescriptor_N_K(bs_ns_ks_lengths[i], bs_ns_ks_strides[i]);
+            },
+            Number<NumBTensor>{});
+    }
+
+    // assume E[M0, M1, M2, ..., N0, N1, N2...]
+    static auto MakeEGridDescriptor_M_N(const std::vector<index_t>& e_ms_ns_lengths_,
+                                        const std::vector<index_t>& e_ms_ns_strides_)
+    {
+        assert(e_ms_ns_lengths_.size() == NumDimM + NumDimN &&
+               e_ms_ns_strides_.size() == NumDimM + NumDimN);
+
+        const auto to_tuple = [&](auto& vec, auto num) {
+            return generate_tuple([&](auto i) { return vec[i]; }, num);
+        };
+
+        const auto e_ms_ns_lengths = to_tuple(e_ms_ns_lengths_, Number<NumDimM + NumDimN>{});
+        const auto e_ms_ns_strides = to_tuple(e_ms_ns_strides_, Number<NumDimM + NumDimN>{});
+
+        // dimension Ids for M0, M1, ...
+        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
+
+        // dimension Ids for N0, N1, ...
+        constexpr auto nDimIds =
+            typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimN, 1>::type{};
+
+        // lengths for M0, M1, ...
+        const auto mLengths = get_container_subset(e_ms_ns_lengths, mDimIds);
+
+        // lengths for K0, K1, ...
+        const auto nLengths = get_container_subset(e_ms_ns_lengths, nDimIds);
+
+        // naive tensor E[M0, M1, M2, ..., N0, N1, N2...]
+        const auto e_grid_desc_ms_ns =
+            make_naive_tensor_descriptor(e_ms_ns_lengths, e_ms_ns_strides);
+
+        // transformed tensor E[MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 * N2 * ...]
+        const auto e_grid_desc_mraw_nraw = transform_tensor_descriptor(
+            e_grid_desc_ms_ns,
+            make_tuple(make_merge_transform(mLengths), make_merge_transform(nLengths)),
+            make_tuple(mDimIds, nDimIds),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
+    }
+
+    static auto
+    MakeDsGridDescriptor_M_N(const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
+                             const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                return MakeEGridDescriptor_M_N(ds_ms_ns_lengths[i], ds_ms_ns_strides[i]);
+            },
+            Number<NumDTensor>{});
+    }
+
+    // desc for problem definition
+    using AsGridDesc_M_K = remove_cvref_t<decltype(MakeAsGridDescriptor_M_K({}, {}))>;
+    using BsGridDesc_N_K = remove_cvref_t<decltype(MakeBsGridDescriptor_N_K({}, {}))>;
+    using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}))>;
+    using EGridDesc_M_N  = remove_cvref_t<decltype(MakeEGridDescriptor_M_N({}, {}))>;
+
+    // desc for blockwise copy
+    using AsGridDesc_AK0_M_AK1 =
+        remove_cvref_t<decltype(GridwiseGemm::MakeAsGridDescriptor_AK0_M_AK1(AsGridDesc_M_K{}))>;
+    using BsGridDesc_BK0_N_BK1 =
+        remove_cvref_t<decltype(GridwiseGemm::MakeBsGridDescriptor_BK0_N_BK1(BsGridDesc_N_K{}))>;
+    using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<
+        decltype(GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+            DsGridDesc_M_N{}))>;
+    using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
+        remove_cvref_t<decltype(GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+            EGridDesc_M_N{}))>;
+
+    // block-to-e-tile map
+    using Block2ETileMap =
+        remove_cvref_t<decltype(GridwiseGemm::MakeBlock2ETileMap(EGridDesc_M_N{}))>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(std::array<const void*, NumATensor> p_as_grid,
+                 std::array<const void*, NumBTensor> p_bs_grid,
+                 std::array<const void*, NumDTensor> p_ds_grid,
+                 void* p_e_grid,
+                 const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_lengths,
+                 const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_strides,
+                 const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_lengths,
+                 const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_strides,
+                 const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_lengths,
+                 const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_strides,
+                 const std::vector<index_t>& e_ms_ns_length,
+                 const std::vector<index_t>& e_ms_ns_stride,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CDEElementwiseOperation cde_element_op)
+            : p_as_grid_{},
+              p_bs_grid_{},
+              p_ds_grid_{},
+              p_e_grid_{static_cast<EDataType*>(p_e_grid)},
+              as_grid_desc_m_k_{},
+              bs_grid_desc_n_k_{},
+              ds_grid_desc_m_n_{},
+              e_grid_desc_m_n_{MakeEGridDescriptor_M_N(e_ms_ns_length, e_ms_ns_stride)},
+              as_grid_desc_ak0_m_ak1_{},
+              bs_grid_desc_bk0_n_bk1_{},
+              ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              e_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              block_2_etile_map_{GridwiseGemm::MakeBlock2ETileMap(e_grid_desc_m_n_)},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              cde_element_op_{cde_element_op}
+        {
+            // populate pointer, desc for As
+            static_for<0, NumATensor, 1>{}([&](auto i) {
+                // using ALayout   = remove_cvref_t<tuple_element_t<i.value, AsLayout>>;
+                using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
+
+                // A pointer
+                p_as_grid_(i) = static_cast<const ADataType*>(p_as_grid[i]);
+
+                // A desc
+                as_grid_desc_m_k_(i) =
+                    MakeAGridDescriptor_M_K(a_ms_ks_lengths[i], a_ms_ks_strides[i]);
+            });
+
+            // populate pointer, desc for Bs
+            static_for<0, NumBTensor, 1>{}([&](auto i) {
+                // using BLayout   = remove_cvref_t<tuple_element_t<i.value, BsLayout>>;
+                using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
+
+                // B pointer
+                p_bs_grid_(i) = static_cast<const BDataType*>(p_bs_grid[i]);
+
+                // B desc
+                bs_grid_desc_n_k_(i) =
+                    MakeBGridDescriptor_N_K(b_ns_ks_lengths[i], b_ns_ks_strides[i]);
+            });
+
+            // populate pointer, desc for Ds
+            static_for<0, NumDTensor, 1>{}([&](auto i) {
+                // using DLayout   = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
+                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
+
+                // D pointer
+                p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
+
+                // D desc
+                ds_grid_desc_m_n_(i) =
+                    MakeEGridDescriptor_M_N(d_ms_ns_lengths[i], d_ms_ns_strides[i]);
+            });
+
+            // populate desc for Ds/E
+            if(GridwiseGemm::CheckValidity(as_grid_desc_m_k_,
+                                           bs_grid_desc_n_k_,
+                                           ds_grid_desc_m_n_,
+                                           e_grid_desc_m_n_,
+                                           block_2_etile_map_))
+            {
+                as_grid_desc_ak0_m_ak1_ =
+                    GridwiseGemm::MakeAsGridDescriptor_AK0_M_AK1(as_grid_desc_m_k_);
+
+                bs_grid_desc_bk0_n_bk1_ =
+                    GridwiseGemm::MakeBsGridDescriptor_BK0_N_BK1(bs_grid_desc_n_k_);
+
+                ds_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        ds_grid_desc_m_n_);
+
+                e_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        e_grid_desc_m_n_);
+            }
+
+            // for sanity check of vector memory access
+            for(index_t i = 0; i < NumATensor; ++i)
+            {
+                a_mz_stride_[i] = a_ms_ks_strides[i][NumDimM - 1];
+                a_kz_stride_[i] = a_ms_ks_strides[i][NumDimM + NumDimK - 1];
+            }
+
+            for(index_t i = 0; i < NumBTensor; ++i)
+            {
+                b_nz_stride_[i] = b_ns_ks_strides[i][NumDimN - 1];
+                b_kz_stride_[i] = b_ns_ks_strides[i][NumDimN + NumDimK - 1];
+            }
+
+            for(index_t i = 0; i < NumDTensor; ++i)
+            {
+                ds_nz_stride_[i] = d_ms_ns_strides[i][NumDimM + NumDimN - 1];
+            }
+
+            e_nz_stride_ = e_ms_ns_stride[NumDimM + NumDimN - 1];
+        }
+
+        // pointers
+        typename GridwiseGemm::AsGridPointer p_as_grid_;
+        typename GridwiseGemm::BsGridPointer p_bs_grid_;
+        typename GridwiseGemm::DsGridPointer p_ds_grid_;
+        EDataType* p_e_grid_;
+
+        // tensor descriptors for problem definiton
+        AsGridDesc_M_K as_grid_desc_m_k_;
+        BsGridDesc_N_K bs_grid_desc_n_k_;
+        DsGridDesc_M_N ds_grid_desc_m_n_;
+        EGridDesc_M_N e_grid_desc_m_n_;
+
+        // tensor descriptors for block/thread-wise copy
+        AsGridDesc_AK0_M_AK1 as_grid_desc_ak0_m_ak1_;
+        BsGridDesc_BK0_N_BK1 bs_grid_desc_bk0_n_bk1_;
+        DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+            ds_grid_desc_mblock_mperblock_nblock_nperblock_;
+        EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
+
+        // block-to-e-tile map
+        Block2ETileMap block_2_etile_map_;
+
+        // element-wise op
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CDEElementwiseOperation cde_element_op_;
+
+        // Strides for the last M/N/K dimensions of A/B/Ds/E
+        //   for sanity check of vector load/store
+        std::array<index_t, NumATensor> a_mz_stride_;
+        std::array<index_t, NumATensor> a_kz_stride_;
+
+        std::array<index_t, NumBTensor> b_nz_stride_;
+        std::array<index_t, NumBTensor> b_kz_stride_;
+
+        std::array<index_t, NumDTensor> ds_nz_stride_;
+        index_t e_nz_stride_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            if(!GridwiseGemm::CheckValidity(arg.as_grid_desc_m_k_,
+                                            arg.bs_grid_desc_n_k_,
+                                            arg.ds_grid_desc_m_n_,
+                                            arg.e_grid_desc_m_n_,
+                                            arg.block_2_etile_map_))
+            {
+                throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
+
+            auto launch_kernel = [&](auto has_main_k_block_loop) {
+                constexpr bool has_main_loop = has_main_k_block_loop.value;
+
+                const auto kernel = kernel_contraction_multiple_abd_xdl_cshuffle<
+                    GridwiseGemm,
+                    typename GridwiseGemm::AsGridPointer,
+                    typename GridwiseGemm::BsGridPointer,
+                    typename GridwiseGemm::DsGridPointer,
+                    EDataType,
+                    AElementwiseOperation,
+                    BElementwiseOperation,
+                    CDEElementwiseOperation,
+                    DeviceOp::AsGridDesc_AK0_M_AK1,
+                    DeviceOp::BsGridDesc_BK0_N_BK1,
+                    DeviceOp::DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                    DeviceOp::EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                    DeviceOp::Block2ETileMap,
+                    has_main_loop>;
+
+                return launch_and_time_kernel(stream_config,
+                                              kernel,
+                                              dim3(grid_size),
+                                              dim3(BlockSize),
+                                              0,
+                                              arg.p_as_grid_,
+                                              arg.p_bs_grid_,
+                                              arg.p_ds_grid_,
+                                              arg.p_e_grid_,
+                                              arg.a_element_op_,
+                                              arg.b_element_op_,
+                                              arg.cde_element_op_,
+                                              arg.as_grid_desc_ak0_m_ak1_,
+                                              arg.bs_grid_desc_bk0_n_bk1_,
+                                              arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                              arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                              arg.block_2_etile_map_);
+            };
+
+            const auto K = arg.as_grid_desc_m_k_[I0].GetLength(I1);
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                return launch_kernel(integral_constant<bool, true>{});
+            }
+            else
+            {
+                return launch_kernel(integral_constant<bool, false>{});
+            }
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if(!ck::is_xdl_supported())
+        {
+            return false;
+        }
+
+        // check vector load/store
+        {
+            bool all_valid = true;
+
+            static_for<0, NumATensor, 1>{}([&](auto i) {
+                // vector memory access of A: could be on M or AK1 dimension
+                if constexpr(ABlockTransferSrcVectorDim == 1)
+                {
+                    if(!(arg.a_mz_stride_[i] == 1 && arg.as_grid_desc_ak0_m_ak1_[i].GetLength(I1) %
+                                                             ABlockTransferSrcScalarPerVector ==
+                                                         0))
+                    {
+                        all_valid = false;
+                    }
+                }
+                else
+                {
+                    if(!(arg.a_kz_stride_[i] == 1 && arg.as_grid_desc_ak0_m_ak1_[i].GetLength(I2) %
+                                                             ABlockTransferSrcScalarPerVector ==
+                                                         0))
+                    {
+                        all_valid = false;
+                    }
+                }
+            });
+
+            // vector memory access of B: could be on N or BK1 dimension
+            static_for<0, NumBTensor, 1>{}([&](auto i) {
+                if constexpr(BBlockTransferSrcVectorDim == 1)
+                {
+                    if(!(arg.b_nz_stride_[i] == 1 && arg.bs_grid_desc_bk0_n_bk1_[i].GetLength(I1) %
+                                                             BBlockTransferSrcScalarPerVector ==
+                                                         0))
+                    {
+                        all_valid = false;
+                    }
+                }
+                else
+                {
+                    if(!(arg.b_kz_stride_[i] == 1 && arg.bs_grid_desc_bk0_n_bk1_[i].GetLength(I2) %
+                                                             BBlockTransferSrcScalarPerVector ==
+                                                         0))
+                    {
+                        all_valid = false;
+                    }
+                }
+            });
+
+            // check vector load of Ds
+            static_for<0, NumDTensor, 1>{}([&](auto i) {
+                if(!(arg.ds_nz_stride_[i] == 1 &&
+                     arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_[i].GetLength(I3) %
+                             CDEBlockTransferScalarPerVector_NPerBlock ==
+                         0))
+                {
+                    all_valid = false;
+                }
+            });
+
+            // vector memory access of E: always on NPerBlock dimension
+            if(!(arg.e_nz_stride_ == 1 &&
+                 arg.e_grid_desc_mblock_mperblock_nblock_nperblock_.GetLength(I3) %
+                         CDEBlockTransferScalarPerVector_NPerBlock ==
+                     0))
+            {
+                all_valid = false;
+            }
+
+            if(!all_valid)
+            {
+                return false;
+            }
+        }
+
+        return GridwiseGemm::CheckValidity(arg.as_grid_desc_m_k_,
+                                           arg.bs_grid_desc_n_k_,
+                                           arg.ds_grid_desc_m_n_,
+                                           arg.e_grid_desc_m_n_,
+                                           arg.block_2_etile_map_);
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(std::array<const void*, NumATensor> p_as,
+                             std::array<const void*, NumBTensor> p_bs,
+                             std::array<const void*, NumDTensor> p_ds,
+                             void* p_e,
+                             const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_lengths,
+                             const std::array<std::vector<index_t>, NumATensor>& a_ms_ks_strides,
+                             const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_lengths,
+                             const std::array<std::vector<index_t>, NumBTensor>& b_ns_ks_strides,
+                             const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_lengths,
+                             const std::array<std::vector<index_t>, NumDTensor>& d_ms_ns_strides,
+                             const std::vector<index_t>& e_ms_ns_length,
+                             const std::vector<index_t>& e_ms_ns_stride,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CDEElementwiseOperation cde_element_op)
+    {
+        return Argument{p_as,
+                        p_bs,
+                        p_ds,
+                        p_e,
+                        a_ms_ks_lengths,
+                        a_ms_ks_strides,
+                        b_ns_ks_lengths,
+                        b_ns_ks_strides,
+                        d_ms_ns_lengths,
+                        d_ms_ns_strides,
+                        e_ms_ns_length,
+                        e_ms_ns_stride,
+                        a_element_op,
+                        b_element_op,
+                        cde_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(std::array<const void*, NumATensor> p_as,
+                        std::array<const void*, NumBTensor> p_bs,
+                        std::array<const void*, NumDTensor> p_ds,
+                        void* p_e,
+                        const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_lengths,
+                        const std::array<std::vector<index_t>, NumATensor>& as_ms_ks_strides,
+                        const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_lengths,
+                        const std::array<std::vector<index_t>, NumBTensor>& bs_ns_ks_strides,
+                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
+                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides,
+                        const std::vector<index_t>& e_ms_ns_length,
+                        const std::vector<index_t>& e_ms_ns_stride,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation cde_element_op) override
+    {
+        return std::make_unique<Argument>(p_as,
+                                          p_bs,
+                                          p_ds,
+                                          p_e,
+                                          as_ms_ks_lengths,
+                                          as_ms_ks_strides,
+                                          bs_ns_ks_lengths,
+                                          bs_ns_ks_strides,
+                                          ds_ms_ns_lengths,
+                                          ds_ms_ns_strides,
+                                          e_ms_ns_length,
+                                          e_ms_ns_stride,
+                                          a_element_op,
+                                          b_element_op,
+                                          cde_element_op);
+    }
+
+    // polymorphic
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    // polymorphic
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        std::map<LoopScheduler, std::string> LoopSchedToString{
+            {LoopScheduler::Default, "Default"}, {LoopScheduler::Interwave, "Interwave"}};
+
+        std::map<PipelineVersion, std::string> PipelineVersionToString{{PipelineVersion::v1, "v1"},
+                                                                       {PipelineVersion::v2, "v2"}};
+
+        // clang-format off
+        str << "DeviceContractionMultipleABD_Xdl_CShuffle"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << AK1 << ", "
+            << BK1 << ", "
+            << MPerXDL << ", "
+            << NPerXDL << ", "
+            << MXdlPerWave << ", "
+            << NXdlPerWave << ", "
+            << ABlockTransferSrcScalarPerVector << ", "
+            << BBlockTransferSrcScalarPerVector << ", "
+            << CShuffleMXdlPerWavePerShuffle << ", "
+            << CShuffleNXdlPerWavePerShuffle << ", "
+            << getGemmSpecializationString(GemmSpec)
+            << ">"
+            << " LoopScheduler: "
+            << LoopSchedToString[LoopSched] << ", "
+            << "PipelineVersion: "
+            << PipelineVersionToString[PipelineVer];
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

@@ -296,6 +296,28 @@ struct DeviceElementwiseImpl
     {
         return std::make_unique<Invoker>();
     };
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceElementwiseImpl<" ;
+        str << "NumDim_" << NumDim << ","; 
+	str << "MPerThread_" << MPerThread << ","; 
+
+        str << "InScalarPerVector"; 
+        static_for<0, InScalarPerVectorSeq::Size(), 1>{}([&](auto i) { str << "_" << InScalarPerVectorSeq::At(i).value; });
+        str << ","; 
+        str << "OutScalarPerVector"; 
+        static_for<0, OutScalarPerVectorSeq::Size(), 1>{}([&](auto i) { str << "_" << OutScalarPerVectorSeq::At(i).value; });
+
+        str << ">";
+        // clang-format on
+
+        return str.str();
+    }
+
 }; // namespace device
 
 } // namespace device

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

@@ -28,6 +28,7 @@ template <typename XDataType,
           typename BetaDataType,
           typename ComputeDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename YElementwiseOperation,
           index_t Rank,
           index_t NumReduceDim,
@@ -43,12 +44,13 @@ template <typename XDataType,
           index_t BetaSrcVectorDim,
           index_t BetaSrcVectorSize,
           index_t YDstVectorSize,
+          index_t SaveMeanInvStdDstVectorSize,
           bool UseWelford = true>
 struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                                                             GammaDataType,
                                                             BetaDataType,
-                                                            ComputeDataType,
                                                             YDataType,
+                                                            SaveMeanInvStdDataType,
                                                             YElementwiseOperation,
                                                             Rank,
                                                             NumReduceDim>
@@ -64,18 +66,24 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
          (BetaSrcVectorDim == 1 && KThreadSliceSize % BetaSrcVectorSize == 0)),
         "Invalid thread slice sizes and/or beta vector sizes configuration, please check!");
 
+    static_assert(MThreadSliceSize % SaveMeanInvStdDstVectorSize == 0,
+                  "Invalid thread slice sizes and/or save mean and inverse std vector sizes "
+                  "configuration, please check!");
+
     using PassThrough = tensor_operation::element_wise::PassThrough;
 
+    static constexpr index_t NumInvariantDim = Rank - NumReduceDim;
     static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
     static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
 
+    static constexpr bool reduceAllDim = (NumInvariantDim == 0);
+    static_assert(!reduceAllDim); // TODO
+
     static auto MakeSrc2dDescriptor(const std::vector<index_t>& inLengths,
                                     const std::vector<index_t>& inStrides,
                                     int numBlockTileIteration)
     {
-        constexpr index_t NumInvariantDim  = Rank - NumReduceDim;
         static constexpr index_t numSrcDim = Rank;
-        static constexpr bool reduceAllDim = (NumInvariantDim == 0);
 
         const auto tupleSrcLengths = make_tuple_from_array(inLengths, Number<numSrcDim>{});
         const auto tupleSrcStrides = make_tuple_from_array(inStrides, Number<numSrcDim>{});
@@ -133,7 +141,37 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
         return (in_grid_desc_m_k_padded);
     };
 
+    static auto MakeSaveMeanInvStdDescriptor_M(const std::vector<index_t>& lengths,
+                                               const std::vector<index_t>& strides)
+    {
+        using InvariantDims = typename arithmetic_sequence_gen<0, NumInvariantDim, 1>::type;
+
+        const auto tupleSrcLengths = make_tuple_from_array_and_index_seq(lengths, InvariantDims{});
+        const auto tupleSrcStrides = make_tuple_from_array_and_index_seq(strides, InvariantDims{});
+
+        const auto desc = make_naive_tensor_descriptor(tupleSrcLengths, tupleSrcStrides);
+
+        const auto grid_desc_m =
+            transform_tensor_descriptor(desc,
+                                        make_tuple(make_merge_transform(tupleSrcLengths)),
+                                        make_tuple(InvariantDims{}),
+                                        make_tuple(Sequence<0>{}));
+
+        const auto invariantLength = grid_desc_m.GetLength(Number<0>{});
+        const auto pad_M =
+            math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
+
+        auto grid_desc_m_padded = transform_tensor_descriptor(
+            grid_desc_m,
+            make_tuple(make_right_pad_transform(invariantLength, pad_M)),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0>{}));
+
+        return grid_desc_m_padded;
+    }
+
     using GridDesc_M_K = decltype(MakeSrc2dDescriptor({1}, {1}, 1));
+    using GridDesc_M   = decltype(MakeSaveMeanInvStdDescriptor_M({1}, {1}));
 
     struct Argument : public BaseArgument
     {
@@ -142,17 +180,23 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                  const std::vector<index_t> gammaStrides,
                  const std::vector<index_t> betaStrides,
                  const std::vector<index_t> yStrides,
+                 const std::vector<index_t> saveMeanStrides,
+                 const std::vector<index_t> saveInvStdStrides,
                  const std::vector<index_t> reduceDims,
                  YElementwiseOperation y_elementwise_op,
                  double epsilon,
                  const XDataType* p_x,
                  const GammaDataType* p_gamma,
                  const BetaDataType* p_beta,
-                 YDataType* p_y)
+                 YDataType* p_y,
+                 SaveMeanInvStdDataType* p_saveMean,
+                 SaveMeanInvStdDataType* p_saveInvStd)
             : p_x_(p_x),
               p_gamma_(p_gamma),
               p_beta_(p_beta),
               p_y_(p_y),
+              p_saveMean_(p_saveMean),
+              p_saveInvStd_(p_saveInvStd),
               y_elementwise_op_(y_elementwise_op)
         {
             epsilon_ = static_cast<ComputeDataType>(epsilon);
@@ -162,16 +206,14 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
             yStrides_     = shuffle_tensor_dimensions<Rank, NumReduceDim>(yStrides, reduceDims);
             gammaStrides_ = shuffle_tensor_dimensions<Rank, NumReduceDim>(gammaStrides, reduceDims);
             betaStrides_  = shuffle_tensor_dimensions<Rank, NumReduceDim>(betaStrides, reduceDims);
+            saveMeanStrides_   = saveMeanStrides;
+            saveInvStdStrides_ = saveInvStdStrides;
 
-            long_index_t invariant_length;
-            long_index_t reduce_length;
-
-            std::tie(invariant_length, reduce_length) =
-                get_2d_lengths<Rank, NumReduceDim>(Lengths_);
+            std::tie(MRaw_, KRaw_) = get_2d_lengths<Rank, NumReduceDim>(Lengths_);
 
-            numBlockTileIteration_ = math::integer_divide_ceil(reduce_length, K_BlockTileSize);
+            numBlockTileIteration_ = math::integer_divide_ceil(KRaw_, K_BlockTileSize);
 
-            gridSize_ = math::integer_divide_ceil(invariant_length, M_BlockTileSize);
+            gridSize_ = math::integer_divide_ceil(MRaw_, M_BlockTileSize);
 
             x_grid_desc_m_k_ = MakeSrc2dDescriptor(Lengths_, xStrides_, numBlockTileIteration_);
             gamma_grid_desc_m_k_ =
@@ -179,9 +221,16 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
             beta_grid_desc_m_k_ =
                 MakeSrc2dDescriptor(Lengths_, betaStrides_, numBlockTileIteration_);
             y_grid_desc_m_k_ = MakeSrc2dDescriptor(Lengths_, yStrides_, numBlockTileIteration_);
+            save_mean_grid_desc_m_    = MakeSaveMeanInvStdDescriptor_M(Lengths_, saveMeanStrides);
+            save_inv_std_grid_desc_m_ = MakeSaveMeanInvStdDescriptor_M(Lengths_, saveInvStdStrides);
 
             isSweeponce_ =
                 x_grid_desc_m_k_.GetLength(Number<1>{}) <= KThreadClusterSize * KThreadSliceSize;
+
+            if constexpr(NumInvariantDim == 0)
+                invariant_lowest_length_ = 1;
+            else
+                invariant_lowest_length_ = Lengths_[NumInvariantDim - 1];
         }
 
         ComputeDataType epsilon_;
@@ -190,12 +239,16 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
         const GammaDataType* p_gamma_;
         const BetaDataType* p_beta_;
         YDataType* p_y_;
+        SaveMeanInvStdDataType* p_saveMean_;
+        SaveMeanInvStdDataType* p_saveInvStd_;
 
         std::vector<index_t> Lengths_;
         std::vector<index_t> xStrides_;
         std::vector<index_t> gammaStrides_;
         std::vector<index_t> betaStrides_;
         std::vector<index_t> yStrides_;
+        std::vector<index_t> saveMeanStrides_;
+        std::vector<index_t> saveInvStdStrides_;
 
         YElementwiseOperation y_elementwise_op_;
 
@@ -206,7 +259,14 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
         GridDesc_M_K gamma_grid_desc_m_k_;
         GridDesc_M_K beta_grid_desc_m_k_;
         GridDesc_M_K y_grid_desc_m_k_;
+        GridDesc_M save_mean_grid_desc_m_;
+        GridDesc_M save_inv_std_grid_desc_m_;
         bool isSweeponce_;
+
+        index_t MRaw_; // invarient length
+        index_t KRaw_; // reduce length
+
+        index_t invariant_lowest_length_;
     };
 
     struct Invoker : public BaseInvoker
@@ -217,9 +277,11 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                                                            GammaDataType,
                                                            BetaDataType,
                                                            YDataType,
+                                                           SaveMeanInvStdDataType,
                                                            ComputeDataType,
                                                            YElementwiseOperation,
                                                            GridDesc_M_K,
+                                                           GridDesc_M,
                                                            BlockSize,
                                                            MThreadClusterSize,
                                                            KThreadClusterSize,
@@ -233,6 +295,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                                                            BetaSrcVectorSize,
                                                            XYSrcVectorDim,
                                                            YDstVectorSize,
+                                                           SaveMeanInvStdDstVectorSize,
                                                            UseWelford>(arg.isSweeponce_);
 
             float avg_time = 0;
@@ -245,12 +308,16 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                                                arg.gamma_grid_desc_m_k_,
                                                arg.beta_grid_desc_m_k_,
                                                arg.y_grid_desc_m_k_,
+                                               arg.save_mean_grid_desc_m_,
+                                               arg.save_inv_std_grid_desc_m_,
                                                arg.numBlockTileIteration_,
                                                arg.epsilon_,
                                                arg.p_x_,
                                                arg.p_gamma_,
                                                arg.p_beta_,
                                                arg.p_y_,
+                                               arg.p_saveMean_,
+                                               arg.p_saveInvStd_,
                                                arg.y_elementwise_op_);
 
             return (avg_time);
@@ -267,8 +334,6 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
     {
         const Argument* p_arg_ = dynamic_cast<const Argument*>(p_arg);
 
-        constexpr index_t NumInvariantDim = Rank - NumReduceDim;
-
         if constexpr(XYSrcVectorDim == 0)
         {
             if constexpr(NumInvariantDim == 0)
@@ -277,13 +342,15 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
             }
             else
             {
+                printf("!!!! %d\n", p_arg_->invariant_lowest_length_);
+
                 if(p_arg_->xStrides_[NumInvariantDim - 1] != 1)
                     return false;
 
-                if(p_arg_->invariant_lowest_length % XSrcVectorSize != 0)
+                if(p_arg_->invariant_lowest_length_ % XSrcVectorSize != 0)
                     return false;
 
-                if(p_arg_->invariant_lowest_length % YDstVectorSize != 0)
+                if(p_arg_->invariant_lowest_length_ % YDstVectorSize != 0)
                     return false;
             };
         }
@@ -325,7 +392,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
             if(p_arg_->betaStrides_[NumInvariantDim - 1] != 1)
                 return (false);
 
-            if(p_arg_->invariant_lowest_length % BetaSrcVectorSize != 0)
+            if(p_arg_->invariant_lowest_length_ % BetaSrcVectorSize != 0)
                 return (false);
         }
         else // if fastest dim is reduced
@@ -337,6 +404,9 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                 return (false);
         }
 
+        if(p_arg_->invariant_lowest_length_ % SaveMeanInvStdDstVectorSize != 0)
+            return false;
+
         return true;
     };
 
@@ -346,6 +416,8 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                         const std::vector<index_t> gammaStrides,
                         const std::vector<index_t> betaStrides,
                         const std::vector<index_t> yStrides,
+                        const std::vector<index_t> saveMeanStrides,
+                        const std::vector<index_t> saveInvStdStrides,
                         const std::vector<index_t> reduceDims,
                         double epsilon,
                         const void* p_x,
@@ -353,27 +425,30 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                         const void* p_beta,
                         void* p_y,
                         void* p_saveMean,
-                        void* p_saveInvVar,
+                        void* p_saveInvStd,
                         YElementwiseOperation y_elementwise_op) override
     {
-        // TODO
-        // Optional cache of the intermediate results (mean and InvVariance) during the
-        // forward pass could speedup in the backward
-        ignore = p_saveMean;
-        ignore = p_saveInvVar;
+        if(lengths.size() != Rank || xStrides.size() != Rank || gammaStrides.size() != Rank ||
+           betaStrides.size() != Rank || yStrides.size() != Rank ||
+           saveMeanStrides.size() != NumInvariantDim || saveInvStdStrides.size() != NumInvariantDim)
+            throw std::runtime_error("dimension is incorrect");
 
         return std::make_unique<Argument>(lengths,
                                           xStrides,
                                           gammaStrides,
                                           betaStrides,
                                           yStrides,
+                                          saveMeanStrides,
+                                          saveInvStdStrides,
                                           reduceDims,
                                           y_elementwise_op,
                                           epsilon,
                                           static_cast<const XDataType*>(p_x),
                                           static_cast<const GammaDataType*>(p_gamma),
                                           static_cast<const BetaDataType*>(p_beta),
-                                          static_cast<YDataType*>(p_y));
+                                          static_cast<YDataType*>(p_y),
+                                          static_cast<SaveMeanInvStdDataType*>(p_saveMean),
+                                          static_cast<SaveMeanInvStdDataType*>(p_saveInvStd));
     };
 
     std::unique_ptr<BaseInvoker> MakeInvokerPointer() override

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

@@ -19,7 +19,7 @@
 namespace ck {
 template <typename GridwiseWelford,
           typename XDataType,
-          typename MeanVarDataType,
+          typename WorkspaceMeanVarDataType,
           typename ComputeDataType,
           typename XGridDesc_M_K,
           typename MeanVarGridDesc_M_KBlock>
@@ -28,8 +28,8 @@ kernel_normalizationSplitK1st(const XGridDesc_M_K x_grid_desc_m_k,
                               const MeanVarGridDesc_M_KBlock mean_var_grid_desc_m_kblock,
                               index_t num_k_block_tile_iteration,
                               const XDataType* const __restrict__ p_x_global,
-                              MeanVarDataType* const __restrict__ p_welford_mean,
-                              MeanVarDataType* const __restrict__ p_welford_variance,
+                              WorkspaceMeanVarDataType* const __restrict__ p_welford_mean,
+                              WorkspaceMeanVarDataType* const __restrict__ p_welford_variance,
                               int32_t* const __restrict__ p_welford_count)
 {
     GridwiseWelford::Run(x_grid_desc_m_k,
@@ -42,16 +42,18 @@ kernel_normalizationSplitK1st(const XGridDesc_M_K x_grid_desc_m_k,
 };
 
 template <typename GridwiseWelfordNormalization,
-          typename MeanVarDataType,
+          typename WorkspaceMeanVarDataType,
           typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename ComputeDataType,
           typename YElementwiseOperation,
           typename MeanVarGridDesc_M_KBlock,
           typename CountGridDesc_M_KBlock,
-          typename XYGammaBetaGridDesc_M_K>
+          typename XYGammaBetaGridDesc_M_K,
+          typename SaveMeanInvStdGridDesc_M>
 __global__ void
 kernel_normalizationSplitK2nd(const MeanVarGridDesc_M_KBlock mean_var_grid_desc_m_kblock,
                               const CountGridDesc_M_KBlock count_grid_desc_m_kblock,
@@ -59,17 +61,21 @@ kernel_normalizationSplitK2nd(const MeanVarGridDesc_M_KBlock mean_var_grid_desc_
                               const XYGammaBetaGridDesc_M_K gamma_grid_desc_m_k,
                               const XYGammaBetaGridDesc_M_K beta_grid_desc_m_k,
                               const XYGammaBetaGridDesc_M_K y_grid_desc_m_k,
+                              const SaveMeanInvStdGridDesc_M save_mean_grid_desc_m,
+                              const SaveMeanInvStdGridDesc_M save_inv_std_grid_desc_m,
                               index_t num_k_mean_var_count_iteration,
                               index_t num_k_block_tile_iteration,
                               index_t k_grid_size,
                               ComputeDataType epsilon,
-                              const MeanVarDataType* const p_mean_global,
-                              const MeanVarDataType* const p_variance_global,
+                              const WorkspaceMeanVarDataType* const p_mean_global,
+                              const WorkspaceMeanVarDataType* const p_variance_global,
                               const int32_t* const p_welford_count_global,
                               const XDataType* const __restrict__ p_x_global,
                               const GammaDataType* const __restrict__ p_gamma_global,
                               const BetaDataType* const __restrict__ p_beta_global,
                               YDataType* const __restrict__ p_y_global,
+                              SaveMeanInvStdDataType* const __restrict__ p_save_mean_global,
+                              SaveMeanInvStdDataType* const __restrict__ p_save_inv_std_global,
                               const YElementwiseOperation y_elementwise_op)
 {
     GridwiseWelfordNormalization::Run(mean_var_grid_desc_m_kblock,
@@ -78,6 +84,8 @@ kernel_normalizationSplitK2nd(const MeanVarGridDesc_M_KBlock mean_var_grid_desc_
                                       gamma_grid_desc_m_k,
                                       beta_grid_desc_m_k,
                                       y_grid_desc_m_k,
+                                      save_mean_grid_desc_m,
+                                      save_inv_std_grid_desc_m,
                                       num_k_mean_var_count_iteration,
                                       num_k_block_tile_iteration,
                                       k_grid_size,
@@ -89,6 +97,8 @@ kernel_normalizationSplitK2nd(const MeanVarGridDesc_M_KBlock mean_var_grid_desc_
                                       p_gamma_global,
                                       p_beta_global,
                                       p_y_global,
+                                      p_save_mean_global,
+                                      p_save_inv_std_global,
                                       y_elementwise_op);
 };
 } // namespace ck
@@ -107,6 +117,7 @@ template <typename XDataType,
           typename BetaDataType,
           typename ComputeDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename YElementwiseOperation,
           index_t Rank,
           index_t NumReduceDim,
@@ -121,17 +132,18 @@ template <typename XDataType,
           index_t GammaSrcVectorSize,
           index_t BetaSrcVectorDim,
           index_t BetaSrcVectorSize,
-          index_t YDstVectorSize>
+          index_t YDstVectorSize,
+          index_t SaveMeanInvStdDstVectorSize>
 struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
                                                                   GammaDataType,
                                                                   BetaDataType,
-                                                                  ComputeDataType,
                                                                   YDataType,
+                                                                  SaveMeanInvStdDataType,
                                                                   YElementwiseOperation,
                                                                   Rank,
                                                                   NumReduceDim>
 {
-    using MeanVarDataType = ComputeDataType;
+    using WorkspaceMeanVarDataType = SaveMeanInvStdDataType;
 
     static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize);
     static_assert(
@@ -144,22 +156,28 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
          (BetaSrcVectorDim == 1 && KThreadSliceSize % BetaSrcVectorSize == 0)),
         "Invalid thread slice sizes and/or beta vector sizes configuration, please check!");
 
+    static_assert(MThreadSliceSize % SaveMeanInvStdDstVectorSize == 0,
+                  "Invalid thread slice sizes and/or save mean and inverse std vector sizes "
+                  "configuration, please check!");
+
     using PassThrough = tensor_operation::element_wise::PassThrough;
 
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
 
+    static constexpr index_t NumInvariantDim = Rank - NumReduceDim;
     static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
     static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
 
+    static constexpr bool reduceAllDim = (NumInvariantDim == 0);
+    static_assert(!reduceAllDim); // TODO
+
     static auto MakeSrc2dDescriptor(const std::vector<index_t>& inLengths,
                                     const std::vector<index_t>& inStrides,
                                     int kBlockSize,
                                     int numBlockTileIteration)
     {
-        constexpr index_t NumInvariantDim  = Rank - NumReduceDim;
         static constexpr index_t numSrcDim = Rank;
-        static constexpr bool reduceAllDim = (NumInvariantDim == 0);
 
         const auto tupleSrcLengths = make_tuple_from_array(inLengths, Number<numSrcDim>{});
         const auto tupleSrcStrides = make_tuple_from_array(inStrides, Number<numSrcDim>{});
@@ -219,7 +237,7 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
     };
 
     template <typename DoPads, index_t MPerTile, index_t KPerTile>
-    static auto MakeMeanVarDescriptor_M_K(index_t M, index_t K)
+    static auto MakeWorkspaceMeanVarDescriptor_M_K(index_t M, index_t K)
     {
         const auto grid_desc_m_k =
             make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(K, I1));
@@ -227,26 +245,57 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
     }
 
     template <typename DoPads, index_t MPerTile, index_t KPerTile>
-    static auto MakeCountDescriptor_M_K(index_t M, index_t K)
+    static auto MakeWorkspaceCountDescriptor_M_K(index_t M, index_t K)
     {
         const auto grid_desc_m_k =
             make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I0, I1));
         return PadTensorDescriptor(grid_desc_m_k, make_tuple(MPerTile, KPerTile), DoPads{});
     }
 
+    static auto MakeSaveMeanInvStdDescriptor_M(const std::vector<index_t>& lengths,
+                                               const std::vector<index_t>& strides)
+    {
+        using InvariantDims = typename arithmetic_sequence_gen<0, NumInvariantDim, 1>::type;
+
+        const auto tupleSrcLengths = make_tuple_from_array_and_index_seq(lengths, InvariantDims{});
+        const auto tupleSrcStrides = make_tuple_from_array_and_index_seq(strides, InvariantDims{});
+
+        const auto desc = make_naive_tensor_descriptor(tupleSrcLengths, tupleSrcStrides);
+
+        const auto grid_desc_m =
+            transform_tensor_descriptor(desc,
+                                        make_tuple(make_merge_transform(tupleSrcLengths)),
+                                        make_tuple(InvariantDims{}),
+                                        make_tuple(Sequence<0>{}));
+
+        const auto invariantLength = grid_desc_m.GetLength(Number<0>{});
+        const auto pad_M =
+            math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
+
+        auto grid_desc_m_padded = transform_tensor_descriptor(
+            grid_desc_m,
+            make_tuple(make_right_pad_transform(invariantLength, pad_M)),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0>{}));
+
+        return grid_desc_m_padded;
+    }
+
     using SrcGridDesc_M_K = decltype(MakeSrc2dDescriptor({1}, {1}, 1, 1));
     using Kernel1MeanVarGridDesc_M_KBlock =
-        decltype(MakeMeanVarDescriptor_M_K<Sequence<true, false>, 1, 1>(1, 1));
+        decltype(MakeWorkspaceMeanVarDescriptor_M_K<Sequence<true, false>, 1, 1>(1, 1));
 
     using Kernel2MeanVarGridDesc_M_KBlock =
-        decltype(MakeMeanVarDescriptor_M_K<Sequence<true, true>, 1, 1>(1, 1));
+        decltype(MakeWorkspaceMeanVarDescriptor_M_K<Sequence<true, true>, 1, 1>(1, 1));
 
     using Kernel2CountGridDesc_M_KBlock =
-        decltype(MakeCountDescriptor_M_K<Sequence<true, true>, 1, 1>(1, 1));
+        decltype(MakeWorkspaceCountDescriptor_M_K<Sequence<true, true>, 1, 1>(1, 1));
+
+    using SaveMeanInvStdGridDesc_M = decltype(MakeSaveMeanInvStdDescriptor_M({1}, {1}));
 
     using GridwiseWelford = GridwiseNormalizationSplitK1st<XDataType,
                                                            ComputeDataType,
-                                                           MeanVarDataType,
+                                                           WorkspaceMeanVarDataType,
                                                            SrcGridDesc_M_K,
                                                            Kernel1MeanVarGridDesc_M_KBlock,
                                                            BlockSize,
@@ -258,16 +307,18 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
                                                            XSrcVectorSize>;
 
     using GridwiseWelfordNormalization =
-        GridwiseNormalizationSplitK2nd<MeanVarDataType,
+        GridwiseNormalizationSplitK2nd<WorkspaceMeanVarDataType,
                                        XDataType,
                                        GammaDataType,
                                        BetaDataType,
                                        YDataType,
+                                       SaveMeanInvStdDataType,
                                        ComputeDataType,
                                        YElementwiseOperation,
                                        Kernel2MeanVarGridDesc_M_KBlock,
                                        Kernel2CountGridDesc_M_KBlock,
                                        SrcGridDesc_M_K,
+                                       SaveMeanInvStdGridDesc_M,
                                        BlockSize,
                                        MThreadClusterSize,
                                        KThreadClusterSize,
@@ -280,7 +331,8 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
                                        BetaSrcVectorDim,
                                        BetaSrcVectorSize,
                                        XYVectorDim,
-                                       YDstVectorSize>;
+                                       YDstVectorSize,
+                                       SaveMeanInvStdDstVectorSize>;
 
     struct Argument : public BaseArgument
     {
@@ -289,17 +341,23 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
                  const std::vector<index_t> gammaStrides,
                  const std::vector<index_t> betaStrides,
                  const std::vector<index_t> yStrides,
+                 const std::vector<index_t> saveMeanStrides,
+                 const std::vector<index_t> saveInvStdStrides,
                  const std::vector<index_t> reduceDims,
                  YElementwiseOperation y_elementwise_op,
                  double epsilon,
                  const XDataType* p_x,
                  const GammaDataType* p_gamma,
                  const BetaDataType* p_beta,
-                 YDataType* p_y)
+                 YDataType* p_y,
+                 SaveMeanInvStdDataType* p_saveMean,
+                 SaveMeanInvStdDataType* p_saveInvStd)
             : p_x_(p_x),
               p_gamma_(p_gamma),
               p_beta_(p_beta),
               p_y_(p_y),
+              p_saveMean_(p_saveMean),
+              p_saveInvStd_(p_saveInvStd),
               p_workspace_mean_{nullptr},
               p_workspace_var_{nullptr},
               p_workspace_count_{nullptr},
@@ -312,6 +370,8 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
             yStrides_     = shuffle_tensor_dimensions<Rank, NumReduceDim>(yStrides, reduceDims);
             gammaStrides_ = shuffle_tensor_dimensions<Rank, NumReduceDim>(gammaStrides, reduceDims);
             betaStrides_  = shuffle_tensor_dimensions<Rank, NumReduceDim>(betaStrides, reduceDims);
+            saveMeanStrides_   = saveMeanStrides;
+            saveInvStdStrides_ = saveInvStdStrides;
 
             std::tie(MRaw_, KRaw_) = get_2d_lengths<Rank, NumReduceDim>(Lengths_);
 
@@ -346,20 +406,28 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
             y_grid_desc_m_k_ =
                 MakeSrc2dDescriptor(Lengths_, yStrides_, kGridSize_, numBlockTileIteration_);
 
+            save_mean_grid_desc_m_    = MakeSaveMeanInvStdDescriptor_M(Lengths_, saveMeanStrides);
+            save_inv_std_grid_desc_m_ = MakeSaveMeanInvStdDescriptor_M(Lengths_, saveInvStdStrides);
+
             // We don't need to pad in K dimension for Welford1. Set KPerTile 1.
             kernel1_mean_var_grid_desc_m_kblock_ =
-                MakeMeanVarDescriptor_M_K<Sequence<true, false>, M_BlockTileSize, 1>(MRaw_,
-                                                                                     kGridSize_);
+                MakeWorkspaceMeanVarDescriptor_M_K<Sequence<true, false>, M_BlockTileSize, 1>(
+                    MRaw_, kGridSize_);
 
             kernel2_mean_var_grid_desc_m_kblock_ =
-                MakeMeanVarDescriptor_M_K<Sequence<true, true>,
-                                          M_BlockTileSize,
-                                          K_MeanVarCountBlockTileSize>(MRaw_, kGridSize_);
+                MakeWorkspaceMeanVarDescriptor_M_K<Sequence<true, true>,
+                                                   M_BlockTileSize,
+                                                   K_MeanVarCountBlockTileSize>(MRaw_, kGridSize_);
 
             kernel2_count_grid_desc_m_kblock_ =
-                MakeCountDescriptor_M_K<Sequence<true, true>,
-                                        M_BlockTileSize,
-                                        K_MeanVarCountBlockTileSize>(MRaw_, kGridSize_);
+                MakeWorkspaceCountDescriptor_M_K<Sequence<true, true>,
+                                                 M_BlockTileSize,
+                                                 K_MeanVarCountBlockTileSize>(MRaw_, kGridSize_);
+
+            if constexpr(NumInvariantDim == 0)
+                invariant_lowest_length_ = 1;
+            else
+                invariant_lowest_length_ = Lengths_[NumInvariantDim - 1];
         }
 
         ComputeDataType epsilon_;
@@ -368,6 +436,8 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
         const GammaDataType* p_gamma_;
         const BetaDataType* p_beta_;
         YDataType* p_y_;
+        SaveMeanInvStdDataType* p_saveMean_;
+        SaveMeanInvStdDataType* p_saveInvStd_;
         void* p_workspace_mean_;
         void* p_workspace_var_;
         void* p_workspace_count_;
@@ -377,6 +447,8 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
         std::vector<index_t> gammaStrides_;
         std::vector<index_t> betaStrides_;
         std::vector<index_t> yStrides_;
+        std::vector<index_t> saveMeanStrides_;
+        std::vector<index_t> saveInvStdStrides_;
 
         YElementwiseOperation y_elementwise_op_;
 
@@ -389,6 +461,8 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
         SrcGridDesc_M_K gamma_grid_desc_m_k_;
         SrcGridDesc_M_K beta_grid_desc_m_k_;
         SrcGridDesc_M_K y_grid_desc_m_k_;
+        SaveMeanInvStdGridDesc_M save_mean_grid_desc_m_;
+        SaveMeanInvStdGridDesc_M save_inv_std_grid_desc_m_;
 
         Kernel1MeanVarGridDesc_M_KBlock kernel1_mean_var_grid_desc_m_kblock_;
         Kernel2MeanVarGridDesc_M_KBlock kernel2_mean_var_grid_desc_m_kblock_;
@@ -396,6 +470,8 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
 
         index_t MRaw_; // invarient length
         index_t KRaw_; // reduce length
+
+        index_t invariant_lowest_length_;
     };
 
     struct Invoker : public BaseInvoker
@@ -408,60 +484,68 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
 
             auto kernel1 = kernel_normalizationSplitK1st<GridwiseWelford,
                                                          XDataType,
-                                                         MeanVarDataType,
+                                                         WorkspaceMeanVarDataType,
                                                          ComputeDataType,
                                                          SrcGridDesc_M_K,
                                                          Kernel1MeanVarGridDesc_M_KBlock>;
 
             auto kernel2 = kernel_normalizationSplitK2nd<GridwiseWelfordNormalization,
-                                                         MeanVarDataType,
+                                                         WorkspaceMeanVarDataType,
                                                          XDataType,
                                                          GammaDataType,
                                                          BetaDataType,
                                                          YDataType,
+                                                         SaveMeanInvStdDataType,
                                                          ComputeDataType,
                                                          YElementwiseOperation,
                                                          Kernel2MeanVarGridDesc_M_KBlock,
                                                          Kernel2CountGridDesc_M_KBlock,
-                                                         SrcGridDesc_M_K>;
+                                                         SrcGridDesc_M_K,
+                                                         SaveMeanInvStdGridDesc_M>;
 
             float avg_time = 0;
-            avg_time += launch_and_time_kernel(stream_config,
-                                               kernel1,
-                                               dim3(arg.gridSize_),
-                                               dim3(BlockSize),
-                                               0,
-                                               arg.x_grid_desc_m_k_,
-                                               arg.kernel1_mean_var_grid_desc_m_kblock_,
-                                               arg.numBlockTileIteration_,
-                                               arg.p_x_,
-                                               static_cast<MeanVarDataType*>(arg.p_workspace_mean_),
-                                               static_cast<MeanVarDataType*>(arg.p_workspace_var_),
-                                               static_cast<int32_t*>(arg.p_workspace_count_));
-
-            avg_time += launch_and_time_kernel(stream_config,
-                                               kernel2,
-                                               dim3(arg.gridSize_),
-                                               dim3(BlockSize),
-                                               0,
-                                               arg.kernel2_mean_var_grid_desc_m_kblock_,
-                                               arg.kernel2_count_grid_desc_m_kblock_,
-                                               arg.x_grid_desc_m_k_,
-                                               arg.gamma_grid_desc_m_k_,
-                                               arg.beta_grid_desc_m_k_,
-                                               arg.y_grid_desc_m_k_,
-                                               arg.numMeanVarCountIteration_,
-                                               arg.numBlockTileIteration_,
-                                               arg.kGridSize_,
-                                               arg.epsilon_,
-                                               static_cast<MeanVarDataType*>(arg.p_workspace_mean_),
-                                               static_cast<MeanVarDataType*>(arg.p_workspace_var_),
-                                               static_cast<int32_t*>(arg.p_workspace_count_),
-                                               arg.p_x_,
-                                               arg.p_gamma_,
-                                               arg.p_beta_,
-                                               arg.p_y_,
-                                               arg.y_elementwise_op_);
+            avg_time += launch_and_time_kernel(
+                stream_config,
+                kernel1,
+                dim3(arg.gridSize_),
+                dim3(BlockSize),
+                0,
+                arg.x_grid_desc_m_k_,
+                arg.kernel1_mean_var_grid_desc_m_kblock_,
+                arg.numBlockTileIteration_,
+                arg.p_x_,
+                static_cast<WorkspaceMeanVarDataType*>(arg.p_workspace_mean_),
+                static_cast<WorkspaceMeanVarDataType*>(arg.p_workspace_var_),
+                static_cast<int32_t*>(arg.p_workspace_count_));
+
+            avg_time += launch_and_time_kernel(
+                stream_config,
+                kernel2,
+                dim3(arg.gridSize_),
+                dim3(BlockSize),
+                0,
+                arg.kernel2_mean_var_grid_desc_m_kblock_,
+                arg.kernel2_count_grid_desc_m_kblock_,
+                arg.x_grid_desc_m_k_,
+                arg.gamma_grid_desc_m_k_,
+                arg.beta_grid_desc_m_k_,
+                arg.y_grid_desc_m_k_,
+                arg.save_mean_grid_desc_m_,
+                arg.save_inv_std_grid_desc_m_,
+                arg.numMeanVarCountIteration_,
+                arg.numBlockTileIteration_,
+                arg.kGridSize_,
+                arg.epsilon_,
+                static_cast<const WorkspaceMeanVarDataType*>(arg.p_workspace_mean_),
+                static_cast<const WorkspaceMeanVarDataType*>(arg.p_workspace_var_),
+                static_cast<const int32_t*>(arg.p_workspace_count_),
+                arg.p_x_,
+                arg.p_gamma_,
+                arg.p_beta_,
+                arg.p_y_,
+                arg.p_saveMean_,
+                arg.p_saveInvStd_,
+                arg.y_elementwise_op_);
 
             return avg_time;
         };
@@ -482,10 +566,10 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
         int welford_size = pArg_->MRaw_ * pArg_->kGridSize_;
 
         // workspace for welford intermediate mean
-        workspace_size += welford_size * sizeof(MeanVarDataType) + 64;
+        workspace_size += welford_size * sizeof(WorkspaceMeanVarDataType) + 64;
 
         // workspace for welford intermediate variance
-        workspace_size += welford_size * sizeof(MeanVarDataType) + 64;
+        workspace_size += welford_size * sizeof(WorkspaceMeanVarDataType) + 64;
 
         // workspace for welford intermediate count
         workspace_size += pArg_->kGridSize_ * sizeof(int32_t) + 64;
@@ -504,13 +588,13 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
         // setup buffer used for intermediate welford mean
         pArg_->p_workspace_mean_ = static_cast<char*>(pArg_->p_workspace_);
 
-        index_t mean_space_sz = welford_size * sizeof(MeanVarDataType);
+        index_t mean_space_sz = welford_size * sizeof(WorkspaceMeanVarDataType);
         mean_space_sz         = math::integer_least_multiple(mean_space_sz, 64);
 
         // setup buffer used for intermediate welford varirance
         pArg_->p_workspace_var_ = reinterpret_cast<char*>(pArg_->p_workspace_mean_) + mean_space_sz;
 
-        index_t variance_space_sz = welford_size * sizeof(MeanVarDataType);
+        index_t variance_space_sz = welford_size * sizeof(WorkspaceMeanVarDataType);
         variance_space_sz         = math::integer_least_multiple(variance_space_sz, 64);
 
         // setup buffer used for intermediate welford count
@@ -522,8 +606,6 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
     {
         const Argument* p_arg_ = dynamic_cast<const Argument*>(p_arg);
 
-        constexpr index_t NumInvariantDim = Rank - NumReduceDim;
-
         if constexpr(XYVectorDim == 0)
         {
             if constexpr(NumInvariantDim == 0)
@@ -535,10 +617,10 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
                 if(p_arg_->xStrides_[NumInvariantDim - 1] != 1)
                     return false;
 
-                if(p_arg_->invariant_lowest_length % XSrcVectorSize != 0)
+                if(p_arg_->invariant_lowest_length_ % XSrcVectorSize != 0)
                     return false;
 
-                if(p_arg_->invariant_lowest_length % YDstVectorSize != 0)
+                if(p_arg_->invariant_lowest_length_ % YDstVectorSize != 0)
                     return false;
             };
         }
@@ -578,7 +660,7 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
             if(p_arg_->betaStrides_[NumInvariantDim - 1] != 1)
                 return false;
 
-            if(p_arg_->invariant_lowest_length % BetaSrcVectorSize != 0)
+            if(p_arg_->invariant_lowest_length_ % BetaSrcVectorSize != 0)
                 return false;
         }
         else // if fastest dim is reduced
@@ -593,6 +675,9 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
         if(p_arg_->kGridSize_ <= 1)
             return false;
 
+        if(p_arg_->invariant_lowest_length_ % SaveMeanInvStdDstVectorSize != 0)
+            return false;
+
         return true;
     };
 
@@ -602,6 +687,8 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
                         const std::vector<index_t> gammaStrides,
                         const std::vector<index_t> betaStrides,
                         const std::vector<index_t> yStrides,
+                        const std::vector<index_t> saveMeanStrides,
+                        const std::vector<index_t> saveInvStdStrides,
                         const std::vector<index_t> reduceDims,
                         double epsilon,
                         const void* p_x,
@@ -609,27 +696,30 @@ struct DeviceNormalizationSplitKImpl : public DeviceNormalization<XDataType,
                         const void* p_beta,
                         void* p_y,
                         void* p_saveMean,
-                        void* p_saveInvVar,
+                        void* p_saveInvStd,
                         YElementwiseOperation y_elementwise_op) override
     {
-        // TODO
-        // Optional cache of the intermediate results (mean and InvVariance) during the
-        // forward pass could speedup in the backward
-        ignore = p_saveMean;
-        ignore = p_saveInvVar;
+        if(lengths.size() != Rank || xStrides.size() != Rank || gammaStrides.size() != Rank ||
+           betaStrides.size() != Rank || yStrides.size() != Rank ||
+           saveMeanStrides.size() != NumInvariantDim || saveInvStdStrides.size() != NumInvariantDim)
+            throw std::runtime_error("dimension is incorrect");
 
         return std::make_unique<Argument>(lengths,
                                           xStrides,
                                           gammaStrides,
                                           betaStrides,
                                           yStrides,
+                                          saveMeanStrides,
+                                          saveInvStdStrides,
                                           reduceDims,
                                           y_elementwise_op,
                                           epsilon,
                                           static_cast<const XDataType*>(p_x),
                                           static_cast<const GammaDataType*>(p_gamma),
                                           static_cast<const BetaDataType*>(p_beta),
-                                          static_cast<YDataType*>(p_y));
+                                          static_cast<YDataType*>(p_y),
+                                          static_cast<SaveMeanInvStdDataType*>(p_saveMean),
+                                          static_cast<SaveMeanInvStdDataType*>(p_saveInvStd));
     };
 
     std::unique_ptr<BaseInvoker> MakeInvokerPointer() override

include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp

@@ -443,10 +443,11 @@ struct Sigmoid
     __host__ __device__ void operator()(T& y, const T& x) const
     {
         static_assert(is_same<T, float>::value || is_same<T, double>::value ||
-                          is_same<T, ck::half_t>::value,
+                          is_same<T, ck::half_t>::value || is_same<T, int8_t>::value ||
+                          is_same<T, int32_t>::value,
                       "Data type is not supported by this operation!");
-
-        y = 1 / (ck::type_convert<T>(1) + exp(-x));
+        constexpr T one = type_convert<T>(1);
+        y               = one / (one + ck::math::exp(-x));
     };
 };
 
@@ -456,7 +457,8 @@ struct TanH
     __host__ __device__ void operator()(T& y, const T& x) const
     {
         static_assert(is_same<T, float>::value || is_same<T, double>::value ||
-                          is_same<T, ck::half_t>::value,
+                          is_same<T, ck::half_t>::value || is_same<T, int8_t>::value ||
+                          is_same<T, int32_t>::value,
                       "Data type is not supported by this operation!");
 
         y = ck::math::tanh(x);
@@ -482,7 +484,101 @@ struct Swish
         y        = type_convert<Y>(x / (1.f + ck::math::exp(bx)));
     };
 
-    float beta_ = 1.0f;
+    const float beta_;
+};
+
+struct SoftRelu
+{
+    SoftRelu(float alpha = 1.f) : alpha_(alpha){};
+
+    template <typename T>
+    __host__ __device__ void operator()(T& y, const T& x) const
+    {
+        static_assert(is_same<T, float>::value || is_same<T, double>::value ||
+                          is_same<T, half_t>::value || is_same<T, int32_t>::value ||
+                          is_same<T, int8_t>::value,
+                      "Data type is not supported by this operation!");
+        T casted_alpha  = type_convert<T>(alpha_);
+        constexpr T one = type_convert<T>(1);
+        y               = ck::math::log(one + ck::math::exp(x * casted_alpha)) / casted_alpha;
+    }
+    const float alpha_;
+};
+
+struct Power
+{
+    Power(float alpha = 0.f, float beta = 1.f, float gamma = 2.f)
+        : alpha_(alpha), beta_(beta), gamma_(gamma){};
+
+    template <typename T>
+    __host__ __device__ void operator()(T& y, const T& x) const
+    {
+        static_assert(is_same<T, float>::value || is_same<T, double>::value ||
+                          is_same<T, half_t>::value || is_same<T, int32_t>::value ||
+                          is_same<T, int8_t>::value,
+                      "Data type is not supported by this operation!");
+        T casted_alpha     = type_convert<T>(alpha_);
+        T casted_beta      = type_convert<T>(beta_);
+        T casted_gamma     = type_convert<T>(gamma_);
+        T shifted_scaled_x = casted_alpha + casted_beta * x;
+        y                  = ck::math::pow(shifted_scaled_x, casted_gamma);
+    }
+    const float alpha_;
+    const float beta_;
+    const float gamma_;
+};
+
+struct ClippedRelu
+{
+    ClippedRelu(float alpha = 0.f, float beta = 1.f) : alpha_(alpha), beta_(beta){};
+
+    template <typename T>
+    __host__ __device__ void operator()(T& y, const T& x) const
+    {
+        static_assert(is_same<T, float>::value || is_same<T, double>::value ||
+                          is_same<T, half_t>::value || is_same<T, int32_t>::value ||
+                          is_same<T, int8_t>::value,
+                      "Data type is not supported by this operation!");
+        T casted_alpha = type_convert<T>(alpha_);
+        T casted_beta  = type_convert<T>(beta_);
+        y              = ck::math::min(casted_beta, ck::math::max(casted_alpha, x));
+    }
+    const float alpha_;
+    const float beta_;
+};
+
+struct LeakyRelu
+{
+    LeakyRelu(float alpha = 0.01f) : alpha_(alpha){};
+
+    template <typename T>
+    __host__ __device__ void operator()(T& y, const T& x) const
+    {
+        static_assert(is_same<T, float>::value || is_same<T, double>::value ||
+                          is_same<T, half_t>::value || is_same<T, int32_t>::value ||
+                          is_same<T, int8_t>::value,
+                      "Data type is not supported by this operation!");
+        T casted_alpha = type_convert<T>(alpha_);
+        y              = x >= 0 ? x : x * casted_alpha;
+    }
+    const float alpha_;
+};
+
+struct Elu
+{
+    Elu(float alpha = 1.f) : alpha_(alpha){};
+
+    template <typename T>
+    __host__ __device__ void operator()(T& y, const T& x) const
+    {
+        static_assert(is_same<T, float>::value || is_same<T, double>::value ||
+                          is_same<T, half_t>::value || is_same<T, int32_t>::value ||
+                          is_same<T, int8_t>::value,
+                      "Data type is not supported by this operation!");
+        T casted_alpha = type_convert<T>(alpha_);
+        y              = x > 0 ? x : casted_alpha * ck::math::expm1(x);
+    }
+    const float alpha_;
 };
 
 } // namespace element_wise

include/ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_naive_variance.hpp

@@ -18,9 +18,11 @@ template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename ComputeDataType,
           typename YElementwiseOperation,
           typename GridDesc_M_K,
+          typename GridDesc_M,
           index_t BlockSize,
           index_t MThreadClusterSize,
           index_t KThreadClusterSize,
@@ -34,6 +36,7 @@ template <typename XDataType,
           index_t BetaSrcVectorSize,
           index_t YDstVectorDim,
           index_t YDstVectorSize,
+          index_t SaveMeanInvStdDstVectorSize,
           bool SweepOnce>
 struct GridwiseNormalizationNaiveVariance_mk_to_mk
 {
@@ -45,6 +48,10 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                       (YDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
                   "Invalid thread slice sizes and/or vector sizes configuration, please check!");
 
+    static_assert(MThreadSliceSize % SaveMeanInvStdDstVectorSize == 0,
+                  "Invalid thread slice sizes and/or save mean and inverse std vector sizes "
+                  "configuration, please check!");
+
     static_assert(XSrcVectorSize == YDstVectorSize);
     static_assert(XSrcVectorSize == GammaSrcVectorSize);
     static_assert(XSrcVectorSize == BetaSrcVectorSize);
@@ -66,6 +73,10 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
     static constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
         make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{}));
 
+    using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
+    static constexpr auto thread_buffer_desc_m =
+        make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
+
     using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
         make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{})));
     using ThreadReduceDstDesc_M =
@@ -84,6 +95,8 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                                                     reduce::Add,
                                                     true>;
 
+    using PassThroughOp = tensor_operation::element_wise::PassThrough;
+
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
@@ -98,12 +111,16 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                                const GridDesc_M_K& gamma_grid_desc_m_k,
                                const GridDesc_M_K& beta_grid_desc_m_k,
                                const GridDesc_M_K& y_grid_desc_m_k,
+                               const GridDesc_M& save_mean_grid_desc_m,
+                               const GridDesc_M& save_inv_std_grid_desc_m,
                                index_t num_k_block_tile_iteration,
                                ComputeDataType epsilon,
                                const XDataType* const __restrict__ p_x_global,
                                const GammaDataType* const __restrict__ p_gamma_global,
                                const BetaDataType* const __restrict__ p_beta_global,
                                YDataType* const __restrict__ p_y_global,
+                               SaveMeanInvStdDataType* const __restrict__ p_save_mean_global,
+                               SaveMeanInvStdDataType* const __restrict__ p_save_inv_std_global,
                                const YElementwiseOperation y_elementwise_op)
     {
         // LDS
@@ -115,6 +132,12 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
         auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
             p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
 
+        auto save_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_save_mean_global, save_mean_grid_desc_m.GetElementSpaceSize());
+
+        auto save_inv_std_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_save_inv_std_global, save_inv_std_grid_desc_m.GetElementSpaceSize());
+
         auto x_thread_buf = generate_tuple(
             [&](auto) {
                 return StaticBuffer<AddressSpaceEnum::Vgpr,
@@ -152,6 +175,8 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
             mean_square_thread_buf;
         StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>&
             var_thread_buf = mean_square_thread_buf;
+        StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>&
+            inv_std_thread_buf = mean_square_thread_buf;
 
         const index_t thread_local_id = get_thread_local_1d_id();
         const index_t block_global_id = get_block_1d_id();
@@ -228,6 +253,42 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                                  thread_k_cluster_id * YDstVectorSize),
                 y_elementwise_op);
 
+        auto threadwise_mean_store =
+            ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
+                                               SaveMeanInvStdDataType,
+                                               decltype(thread_buffer_desc_m),
+                                               GridDesc_M,
+                                               PassThroughOp,
+                                               ThreadBufferLengths_M,
+                                               Sequence<0>,                 // DimAccessOrder
+                                               0,                           // SrcVectorDim
+                                               SaveMeanInvStdDstVectorSize, // ScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1,
+                                               true>(
+                save_mean_grid_desc_m,
+                make_multi_index(block_global_id * M_BlockTileSize +
+                                 thread_m_cluster_id * MThreadSliceSize),
+                PassThroughOp{});
+
+        auto threadwise_inv_std_store =
+            ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
+                                               SaveMeanInvStdDataType,
+                                               decltype(thread_buffer_desc_m),
+                                               GridDesc_M,
+                                               PassThroughOp,
+                                               ThreadBufferLengths_M,
+                                               Sequence<0>,                 // DimAccessOrder
+                                               0,                           // SrcVectorDim
+                                               SaveMeanInvStdDstVectorSize, // ScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1,
+                                               true>(
+                save_inv_std_grid_desc_m,
+                make_multi_index(block_global_id * M_BlockTileSize +
+                                 thread_m_cluster_id * MThreadSliceSize),
+                PassThroughOp{});
+
         constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
         constexpr auto thread_copy_bwd_step_m_k =
             make_multi_index(0, SweepOnce ? 0 : -K_BlockTileSize);
@@ -243,7 +304,8 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
 
         // E(x), E[x^2], var(x)
         // FIXME: Should not hack the transform from deviceOP
-        int reduce_length = x_grid_desc_m_k.GetTransforms()[I2].GetUpperLengths()[I0];
+        ComputeDataType reduce_length = type_convert<ComputeDataType>(
+            x_grid_desc_m_k.GetTransforms()[I2].GetUpperLengths()[I0]);
 
         static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
             mean_thread_buf(I)        = reduce::Add::template GetIdentityValue<ComputeDataType>();
@@ -302,10 +364,34 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                 // var(x) = E[x^2] - E[x]^2
                 var_thread_buf(I) =
                     mean_square_thread_buf(I) - (mean_thread_buf(I) * mean_thread_buf(I));
+
+                inv_std_thread_buf(I) = type_convert<ComputeDataType>(1.0f) /
+                                        ck::math::sqrt(var_thread_buf(I) + epsilon);
             });
 
+            // save mean and inverse std for backward (optional)
+            if(thread_k_cluster_id == 0)
+            {
+                if(p_save_mean_global != nullptr)
+                {
+                    threadwise_mean_store.Run(thread_buffer_desc_m,
+                                              make_tuple(I0),
+                                              mean_thread_buf,
+                                              save_mean_grid_desc_m,
+                                              save_mean_global_val_buf);
+                }
+                if(p_save_inv_std_global != nullptr)
+                {
+                    threadwise_inv_std_store.Run(thread_buffer_desc_m,
+                                                 make_tuple(I0),
+                                                 inv_std_thread_buf,
+                                                 save_inv_std_grid_desc_m,
+                                                 save_inv_std_global_val_buf);
+                }
+            }
+
+            // normalization
             static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
-                auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
                 static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
                     static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
                         constexpr auto offset_m_k =
@@ -314,7 +400,7 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                         // normalize
                         y_thread_buf(iK0)(Number<offset_m_k>{}) =
                             (x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
-                            divisor;
+                            inv_std_thread_buf(iM);
 
                         // gamma & beta
                         y_thread_buf(iK0)(Number<offset_m_k>{}) =
@@ -404,8 +490,30 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                 // var(x) = E[x^2] - E[x]^2
                 var_thread_buf(I) =
                     mean_square_thread_buf(I) - (mean_thread_buf(I) * mean_thread_buf(I));
+
+                inv_std_thread_buf(I) = 1 / ck::math::sqrt(var_thread_buf(I) + epsilon);
             });
 
+            if(thread_k_cluster_id == 0)
+            {
+                if(p_save_mean_global != nullptr)
+                {
+                    threadwise_mean_store.Run(thread_buffer_desc_m,
+                                              make_tuple(I0),
+                                              mean_thread_buf,
+                                              save_mean_grid_desc_m,
+                                              save_mean_global_val_buf);
+                }
+                if(p_save_inv_std_global != nullptr)
+                {
+                    threadwise_inv_std_store.Run(thread_buffer_desc_m,
+                                                 make_tuple(I0),
+                                                 inv_std_thread_buf,
+                                                 save_inv_std_grid_desc_m,
+                                                 save_inv_std_global_val_buf);
+                }
+            }
+
             auto thread_copy_tail_m_k =
                 (num_k_block_tile_iteration - 1) * ThreadBufferNumber * thread_copy_fwd_step_m_k;
 
@@ -437,7 +545,6 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                 });
 
                 static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
-                    auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
                     static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
                         static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
                             constexpr auto offset_m_k =
@@ -446,7 +553,7 @@ struct GridwiseNormalizationNaiveVariance_mk_to_mk
                             // normalize
                             y_thread_buf(iK0)(Number<offset_m_k>{}) =
                                 (x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
-                                divisor;
+                                inv_std_thread_buf(iM);
 
                             // gamma
                             y_thread_buf(iK0)(Number<offset_m_k>{}) =

include/ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_selector.hpp

@@ -12,31 +12,42 @@ template <typename GridwiseReduction,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename ComputeDataType,
           typename YElementwiseOperation,
-          typename GridDesc_M_K>
-__global__ void kernel_normalization(const GridDesc_M_K x_grid_desc_m_k,
-                                     const GridDesc_M_K gamma_grid_desc_m_k,
-                                     const GridDesc_M_K beta_grid_desc_m_k,
-                                     const GridDesc_M_K y_grid_desc_m_k,
-                                     index_t num_k_block_tile_iteration,
-                                     ComputeDataType epsilon,
-                                     const XDataType* const __restrict__ p_x_global,
-                                     const GammaDataType* const __restrict__ p_gamma_global,
-                                     const BetaDataType* const __restrict__ p_beta_global,
-                                     YDataType* const __restrict__ p_y_global,
-                                     const YElementwiseOperation y_elementwise_op)
+          typename GridDesc_M_K,
+          typename GridDesc_M>
+__global__ void
+kernel_normalization(const GridDesc_M_K x_grid_desc_m_k,
+                     const GridDesc_M_K gamma_grid_desc_m_k,
+                     const GridDesc_M_K beta_grid_desc_m_k,
+                     const GridDesc_M_K y_grid_desc_m_k,
+                     const GridDesc_M save_mean_grid_desc_m,
+                     const GridDesc_M save_inv_std_grid_desc_m,
+                     index_t num_k_block_tile_iteration,
+                     ComputeDataType epsilon,
+                     const XDataType* const __restrict__ p_x_global,
+                     const GammaDataType* const __restrict__ p_gamma_global,
+                     const BetaDataType* const __restrict__ p_beta_global,
+                     YDataType* const __restrict__ p_y_global,
+                     SaveMeanInvStdDataType* const __restrict__ p_save_mean_global,
+                     SaveMeanInvStdDataType* const __restrict__ p_save_inv_std_global,
+                     const YElementwiseOperation y_elementwise_op)
 {
     GridwiseReduction::Run(x_grid_desc_m_k,
                            gamma_grid_desc_m_k,
                            beta_grid_desc_m_k,
                            y_grid_desc_m_k,
+                           save_mean_grid_desc_m,
+                           save_inv_std_grid_desc_m,
                            num_k_block_tile_iteration,
                            epsilon,
                            p_x_global,
                            p_gamma_global,
                            p_beta_global,
                            p_y_global,
+                           p_save_mean_global,
+                           p_save_inv_std_global,
                            y_elementwise_op);
 };
 
@@ -44,9 +55,11 @@ template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename ComputeDataType,
           typename YElementwiseOperation,
           typename GridDesc_M_K,
+          typename GridDesc_M,
           index_t BlockSize,
           index_t MThreadClusterSize,
           index_t KThreadClusterSize,
@@ -60,6 +73,7 @@ template <typename XDataType,
           index_t BetaSrcVectorSize,
           index_t YDstVectorDim,
           index_t YDstVectorSize,
+          index_t SaveMeanInvStdDstVectorSize,
           bool UseWelford>
 auto NormalizationKernelSelector(bool isSweepOnce)
 {
@@ -68,9 +82,11 @@ auto NormalizationKernelSelector(bool isSweepOnce)
                                                     GammaDataType,
                                                     BetaDataType,
                                                     YDataType,
+                                                    SaveMeanInvStdDataType,
                                                     ComputeDataType,
                                                     YElementwiseOperation,
                                                     GridDesc_M_K,
+                                                    GridDesc_M,
                                                     BlockSize,
                                                     MThreadClusterSize,
                                                     KThreadClusterSize,
@@ -84,15 +100,18 @@ auto NormalizationKernelSelector(bool isSweepOnce)
                                                     BetaSrcVectorSize,
                                                     YDstVectorDim,
                                                     YDstVectorSize,
+                                                    SaveMeanInvStdDstVectorSize,
                                                     false>;
     using GridwiseNormalizationSweepOnceNaive =
         GridwiseNormalizationNaiveVariance_mk_to_mk<XDataType,
                                                     GammaDataType,
                                                     BetaDataType,
                                                     YDataType,
+                                                    SaveMeanInvStdDataType,
                                                     ComputeDataType,
                                                     YElementwiseOperation,
                                                     GridDesc_M_K,
+                                                    GridDesc_M,
                                                     BlockSize,
                                                     MThreadClusterSize,
                                                     KThreadClusterSize,
@@ -106,15 +125,18 @@ auto NormalizationKernelSelector(bool isSweepOnce)
                                                     BetaSrcVectorSize,
                                                     YDstVectorDim,
                                                     YDstVectorSize,
+                                                    SaveMeanInvStdDstVectorSize,
                                                     true>;
     using GridwiseNormalizationGenericWelford =
         GridwiseNormalizationWelfordVariance_mk_to_mk<XDataType,
                                                       GammaDataType,
                                                       BetaDataType,
                                                       YDataType,
+                                                      SaveMeanInvStdDataType,
                                                       ComputeDataType,
                                                       YElementwiseOperation,
                                                       GridDesc_M_K,
+                                                      GridDesc_M,
                                                       BlockSize,
                                                       MThreadClusterSize,
                                                       KThreadClusterSize,
@@ -128,15 +150,18 @@ auto NormalizationKernelSelector(bool isSweepOnce)
                                                       BetaSrcVectorSize,
                                                       YDstVectorDim,
                                                       YDstVectorSize,
+                                                      SaveMeanInvStdDstVectorSize,
                                                       false>;
     using GridwiseNormalizationSweepOnceWelford =
         GridwiseNormalizationWelfordVariance_mk_to_mk<XDataType,
                                                       GammaDataType,
                                                       BetaDataType,
                                                       YDataType,
+                                                      SaveMeanInvStdDataType,
                                                       ComputeDataType,
                                                       YElementwiseOperation,
                                                       GridDesc_M_K,
+                                                      GridDesc_M,
                                                       BlockSize,
                                                       MThreadClusterSize,
                                                       KThreadClusterSize,
@@ -150,6 +175,7 @@ auto NormalizationKernelSelector(bool isSweepOnce)
                                                       BetaSrcVectorSize,
                                                       YDstVectorDim,
                                                       YDstVectorSize,
+                                                      SaveMeanInvStdDstVectorSize,
                                                       true>;
 
     if constexpr(UseWelford)
@@ -159,17 +185,21 @@ auto NormalizationKernelSelector(bool isSweepOnce)
                                                   GammaDataType,
                                                   BetaDataType,
                                                   YDataType,
+                                                  SaveMeanInvStdDataType,
                                                   ComputeDataType,
                                                   YElementwiseOperation,
-                                                  GridDesc_M_K>
+                                                  GridDesc_M_K,
+                                                  GridDesc_M>
                            : kernel_normalization<GridwiseNormalizationGenericWelford,
                                                   XDataType,
                                                   GammaDataType,
                                                   BetaDataType,
                                                   YDataType,
+                                                  SaveMeanInvStdDataType,
                                                   ComputeDataType,
                                                   YElementwiseOperation,
-                                                  GridDesc_M_K>;
+                                                  GridDesc_M_K,
+                                                  GridDesc_M>;
     }
     else
     {
@@ -178,17 +208,21 @@ auto NormalizationKernelSelector(bool isSweepOnce)
                                                   GammaDataType,
                                                   BetaDataType,
                                                   YDataType,
+                                                  SaveMeanInvStdDataType,
                                                   ComputeDataType,
                                                   YElementwiseOperation,
-                                                  GridDesc_M_K>
+                                                  GridDesc_M_K,
+                                                  GridDesc_M>
                            : kernel_normalization<GridwiseNormalizationGenericNaive,
                                                   XDataType,
                                                   GammaDataType,
                                                   BetaDataType,
                                                   YDataType,
+                                                  SaveMeanInvStdDataType,
                                                   ComputeDataType,
                                                   YElementwiseOperation,
-                                                  GridDesc_M_K>;
+                                                  GridDesc_M_K,
+                                                  GridDesc_M>;
     }
 }
 

include/ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_splitk_2nd.hpp

@@ -17,11 +17,13 @@ template <typename MeanVarDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename ComputeDataType,
           typename YElementwiseOperation,
           typename MeanVarGridDesc_M_KBlock,
           typename CountGridDesc_M_KBlock,
           typename XYGammaBetaGridDesc_M_K,
+          typename SaveMeanInvStdGridDesc_M,
           index_t BlockSize,
           index_t MThreadClusterSize,
           index_t KThreadClusterSize,
@@ -34,7 +36,8 @@ template <typename MeanVarDataType,
           index_t BetaSrcVectorDim,
           index_t BetaSrcVectorSize,
           index_t YDstVectorDim,
-          index_t YDstVectorSize>
+          index_t YDstVectorSize,
+          index_t SaveMeanInvStdDstVectorSize>
 struct GridwiseNormalizationSplitK2nd
 {
     static_assert((XSrcVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
@@ -45,6 +48,10 @@ struct GridwiseNormalizationSplitK2nd
                       (YDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
                   "Invalid thread slice sizes and/or vector sizes configuration, please check!");
 
+    static_assert(MThreadSliceSize % SaveMeanInvStdDstVectorSize == 0,
+                  "Invalid thread slice sizes and/or save mean and inverse std vector sizes "
+                  "configuration, please check!");
+
     static_assert(XSrcVectorSize == YDstVectorSize);
     static_assert(XSrcVectorSize == GammaSrcVectorSize);
     static_assert(XSrcVectorSize == BetaSrcVectorSize);
@@ -69,6 +76,10 @@ struct GridwiseNormalizationSplitK2nd
     static constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
         make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{}));
 
+    using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
+    static constexpr auto thread_buffer_desc_m =
+        make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
+
     using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
     static constexpr auto thread_buffer_desc_m_1 =
         make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, I1));
@@ -99,6 +110,8 @@ struct GridwiseNormalizationSplitK2nd
                                const XYGammaBetaGridDesc_M_K& gamma_grid_desc_m_k,
                                const XYGammaBetaGridDesc_M_K& beta_grid_desc_m_k,
                                const XYGammaBetaGridDesc_M_K& y_grid_desc_m_k,
+                               const SaveMeanInvStdGridDesc_M& save_mean_grid_desc_m,
+                               const SaveMeanInvStdGridDesc_M& save_inv_std_grid_desc_m,
                                index_t num_k_mean_var_count_iteration,
                                index_t num_k_block_tile_iteration,
                                index_t k_grid_size,
@@ -110,6 +123,8 @@ struct GridwiseNormalizationSplitK2nd
                                const GammaDataType* const __restrict__ p_gamma_global,
                                const BetaDataType* const __restrict__ p_beta_global,
                                YDataType* const __restrict__ p_y_global,
+                               SaveMeanInvStdDataType* const __restrict__ p_save_mean_global,
+                               SaveMeanInvStdDataType* const __restrict__ p_save_inv_std_global,
                                const YElementwiseOperation y_elementwise_op)
     {
         // Thread/Block id
@@ -145,6 +160,12 @@ struct GridwiseNormalizationSplitK2nd
         auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
             p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
 
+        auto save_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_save_mean_global, save_mean_grid_desc_m.GetElementSpaceSize());
+
+        auto save_inv_std_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_save_inv_std_global, save_inv_std_grid_desc_m.GetElementSpaceSize());
+
         // VGPR
         StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
             in_mean_thread_buf;
@@ -158,6 +179,7 @@ struct GridwiseNormalizationSplitK2nd
             var_thread_buf;
         StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
             welford_count_thread_buf;
+        auto& inv_std_thread_buf = var_thread_buf;
 
         auto x_thread_buf = generate_tuple(
             [&](auto) {
@@ -283,6 +305,42 @@ struct GridwiseNormalizationSplitK2nd
                                      thread_k_cluster_id * YDstVectorSize),
                 y_elementwise_op);
 
+        auto threadwise_mean_store =
+            ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
+                                               SaveMeanInvStdDataType,
+                                               decltype(thread_buffer_desc_m),
+                                               SaveMeanInvStdGridDesc_M,
+                                               PassThroughOp,
+                                               ThreadBufferLengths_M,
+                                               Sequence<0>,                 // DimAccessOrder
+                                               0,                           // SrcVectorDim
+                                               SaveMeanInvStdDstVectorSize, // ScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1,
+                                               true>(
+                save_mean_grid_desc_m,
+                make_multi_index(block_m_cluster_id * M_BlockTileSize +
+                                 thread_m_cluster_id * MThreadSliceSize),
+                PassThroughOp{});
+
+        auto threadwise_inv_std_store =
+            ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
+                                               SaveMeanInvStdDataType,
+                                               decltype(thread_buffer_desc_m),
+                                               SaveMeanInvStdGridDesc_M,
+                                               PassThroughOp,
+                                               ThreadBufferLengths_M,
+                                               Sequence<0>,                 // DimAccessOrder
+                                               0,                           // SrcVectorDim
+                                               SaveMeanInvStdDstVectorSize, // ScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1,
+                                               true>(
+                save_inv_std_grid_desc_m,
+                make_multi_index(block_m_cluster_id * M_BlockTileSize +
+                                 thread_m_cluster_id * MThreadSliceSize),
+                PassThroughOp{});
+
         // step1: Merge mean and variance
         constexpr auto mean_var_count_thread_copy_step_I0_k =
             make_multi_index(I0, KThreadClusterSize);
@@ -332,9 +390,33 @@ struct GridwiseNormalizationSplitK2nd
 
             BlockwiseWelford::Run(
                 mean_thread_buf(I), var_thread_buf(I), welford_count_thread_buf(I));
+
+            inv_std_thread_buf(I) =
+                type_convert<ComputeDataType>(1.0f) / ck::math::sqrt(var_thread_buf(I) + epsilon);
         });
 
-        // step2: normalization
+        // step2: save mean and inverse std for backward (optional)
+        if(block_k_cluster_id == 0 && thread_k_cluster_id == 0)
+        {
+            if(p_save_mean_global != nullptr)
+            {
+                threadwise_mean_store.Run(thread_buffer_desc_m,
+                                          make_tuple(I0),
+                                          mean_thread_buf,
+                                          save_mean_grid_desc_m,
+                                          save_mean_global_val_buf);
+            }
+            if(p_save_inv_std_global != nullptr)
+            {
+                threadwise_inv_std_store.Run(thread_buffer_desc_m,
+                                             make_tuple(I0),
+                                             inv_std_thread_buf,
+                                             save_inv_std_grid_desc_m,
+                                             save_inv_std_global_val_buf);
+            }
+        }
+
+        // step3: normalization
         constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
 
         for(index_t k = 0; k < num_k_block_tile_iteration; ++k)
@@ -360,7 +442,6 @@ struct GridwiseNormalizationSplitK2nd
             });
 
             static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
-                auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
                 static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
                     static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
                         constexpr auto offset_m_k =
@@ -369,7 +450,7 @@ struct GridwiseNormalizationSplitK2nd
                         // normalize
                         y_thread_buf(iK0)(Number<offset_m_k>{}) =
                             (x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
-                            divisor;
+                            inv_std_thread_buf(iM);
 
                         // gamma
                         y_thread_buf(iK0)(Number<offset_m_k>{}) =

include/ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_welford_variance.hpp

@@ -16,9 +16,11 @@ template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           typename ComputeDataType,
           typename YElementwiseOperation,
           typename GridDesc_M_K,
+          typename GridDesc_M,
           index_t BlockSize,
           index_t MThreadClusterSize,
           index_t KThreadClusterSize,
@@ -32,6 +34,7 @@ template <typename XDataType,
           index_t BetaSrcVectorSize,
           index_t YDstVectorDim,
           index_t YDstVectorSize,
+          index_t SaveMeanInvStdDstVectorSize,
           bool SweepOnce>
 struct GridwiseNormalizationWelfordVariance_mk_to_mk
 {
@@ -43,6 +46,10 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
                       (YDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
                   "Invalid thread slice sizes and/or vector sizes configuration, please check!");
 
+    static_assert(MThreadSliceSize % SaveMeanInvStdDstVectorSize == 0,
+                  "Invalid thread slice sizes and/or save mean and inverse std vector sizes "
+                  "configuration, please check!");
+
     static_assert(XSrcVectorSize == YDstVectorSize);
     static_assert(XSrcVectorSize == GammaSrcVectorSize);
     static_assert(XSrcVectorSize == BetaSrcVectorSize);
@@ -64,6 +71,10 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
     static constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
         make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{}));
 
+    using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
+    static constexpr auto thread_buffer_desc_m =
+        make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
+
     using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
         make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{})));
     using ThreadReduceDstDesc_M =
@@ -77,6 +88,8 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
                                               ThreadClusterLengths_M_K,
                                               ThreadClusterArrangeOrder>;
 
+    using PassThroughOp = tensor_operation::element_wise::PassThrough;
+
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
@@ -114,17 +127,18 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
                                const GridDesc_M_K& gamma_grid_desc_m_k,
                                const GridDesc_M_K& beta_grid_desc_m_k,
                                const GridDesc_M_K& y_grid_desc_m_k,
+                               const GridDesc_M& save_mean_grid_desc_m,
+                               const GridDesc_M& save_inv_std_grid_desc_m,
                                index_t num_k_block_tile_iteration,
                                ComputeDataType epsilon,
                                const XDataType* const __restrict__ p_x_global,
                                const GammaDataType* const __restrict__ p_gamma_global,
                                const BetaDataType* const __restrict__ p_beta_global,
                                YDataType* const __restrict__ p_y_global,
+                               SaveMeanInvStdDataType* const __restrict__ p_save_mean_global,
+                               SaveMeanInvStdDataType* const __restrict__ p_save_inv_std_global,
                                const YElementwiseOperation y_elementwise_op)
     {
-        auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
-
         auto x_thread_buf = generate_tuple(
             [&](auto) {
                 return StaticBuffer<AddressSpaceEnum::Vgpr,
@@ -150,6 +164,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
             mean_thread_buf;
         StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
             var_thread_buf;
+        auto& inv_std_thread_buf = var_thread_buf;
 
         const index_t thread_local_id = get_thread_local_1d_id();
         const index_t block_global_id = get_block_1d_id();
@@ -226,6 +241,42 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
                                  thread_k_cluster_id * YDstVectorSize),
                 y_elementwise_op);
 
+        auto threadwise_mean_store =
+            ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
+                                               SaveMeanInvStdDataType,
+                                               decltype(thread_buffer_desc_m),
+                                               GridDesc_M,
+                                               PassThroughOp,
+                                               ThreadBufferLengths_M,
+                                               Sequence<0>,                 // DimAccessOrder
+                                               0,                           // SrcVectorDim
+                                               SaveMeanInvStdDstVectorSize, // ScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1,
+                                               true>(
+                save_mean_grid_desc_m,
+                make_multi_index(block_global_id * M_BlockTileSize +
+                                 thread_m_cluster_id * MThreadSliceSize),
+                PassThroughOp{});
+
+        auto threadwise_inv_std_store =
+            ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
+                                               SaveMeanInvStdDataType,
+                                               decltype(thread_buffer_desc_m),
+                                               GridDesc_M,
+                                               PassThroughOp,
+                                               ThreadBufferLengths_M,
+                                               Sequence<0>,                 // DimAccessOrder
+                                               0,                           // SrcVectorDim
+                                               SaveMeanInvStdDstVectorSize, // ScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1,
+                                               true>(
+                save_inv_std_grid_desc_m,
+                make_multi_index(block_global_id * M_BlockTileSize +
+                                 thread_m_cluster_id * MThreadSliceSize),
+                PassThroughOp{});
+
         constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
         constexpr auto thread_copy_bwd_step_m_k =
             make_multi_index(0, SweepOnce ? 0 : -K_BlockTileSize);
@@ -239,6 +290,15 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
         const auto beta_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
             p_beta_global, beta_grid_desc_m_k.GetElementSpaceSize());
 
+        auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
+
+        auto save_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_save_mean_global, save_mean_grid_desc_m.GetElementSpaceSize());
+
+        auto save_inv_std_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_save_inv_std_global, save_inv_std_grid_desc_m.GetElementSpaceSize());
+
         auto threadwise_welford       = ThreadwiseWelford();
         threadwise_welford.max_count_ = GetKPerThread(x_grid_desc_m_k, thread_k_cluster_id);
 
@@ -279,10 +339,33 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
 
                 int count = threadwise_welford.cur_count_;
                 BlockwiseWelford::Run(mean_thread_buf(I), var_thread_buf(I), count);
+                inv_std_thread_buf(I) = type_convert<ComputeDataType>(1.0f) /
+                                        ck::math::sqrt(var_thread_buf(I) + epsilon);
             });
 
+            // save mean and inverse std for backward (optional)
+            if(thread_k_cluster_id == 0)
+            {
+                if(p_save_mean_global != nullptr)
+                {
+                    threadwise_mean_store.Run(thread_buffer_desc_m,
+                                              make_tuple(I0),
+                                              mean_thread_buf,
+                                              save_mean_grid_desc_m,
+                                              save_mean_global_val_buf);
+                }
+                if(p_save_inv_std_global != nullptr)
+                {
+                    threadwise_inv_std_store.Run(thread_buffer_desc_m,
+                                                 make_tuple(I0),
+                                                 inv_std_thread_buf,
+                                                 save_inv_std_grid_desc_m,
+                                                 save_inv_std_global_val_buf);
+                }
+            }
+
+            // normalization
             static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
-                auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
                 static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
                     static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
                         constexpr auto offset_m_k =
@@ -291,7 +374,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
                         // normalize
                         y_thread_buf(iK0)(Number<offset_m_k>{}) =
                             (x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
-                            divisor;
+                            inv_std_thread_buf(iM);
 
                         // gamma & beta
                         y_thread_buf(iK0)(Number<offset_m_k>{}) =
@@ -360,8 +443,29 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
 
                 int count = threadwise_welford.cur_count_;
                 BlockwiseWelford::Run(mean_thread_buf(I), var_thread_buf(I), count);
+                inv_std_thread_buf(I) = 1 / ck::math::sqrt(var_thread_buf(I) + epsilon);
             });
 
+            if(thread_k_cluster_id == 0)
+            {
+                if(p_save_mean_global != nullptr)
+                {
+                    threadwise_mean_store.Run(thread_buffer_desc_m,
+                                              make_tuple(I0),
+                                              mean_thread_buf,
+                                              save_mean_grid_desc_m,
+                                              save_mean_global_val_buf);
+                }
+                if(p_save_inv_std_global != nullptr)
+                {
+                    threadwise_inv_std_store.Run(thread_buffer_desc_m,
+                                                 make_tuple(I0),
+                                                 inv_std_thread_buf,
+                                                 save_inv_std_grid_desc_m,
+                                                 save_inv_std_global_val_buf);
+                }
+            }
+
             auto thread_copy_tail_m_k =
                 (num_k_block_tile_iteration - 1) * ThreadBufferNumber * thread_copy_fwd_step_m_k;
 
@@ -393,7 +497,6 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
                 });
 
                 static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
-                    auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
                     static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
                         static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
                             constexpr auto offset_m_k =
@@ -402,7 +505,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
                             // normalize
                             y_thread_buf(iK0)(Number<offset_m_k>{}) =
                                 (x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
-                                divisor;
+                                inv_std_thread_buf(iM);
 
                             // gamma
                             y_thread_buf(iK0)(Number<offset_m_k>{}) =