Merge pull request #18 from ROCmSoftwarePlatform/merge-from-public

Merge from public

example/40_conv2d_fwd_quantization/conv2d_fwd_xdl_perlayer_quantization_int8.cpp

@@ -2,7 +2,7 @@
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "common.hpp"
-#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
 
 using InDataType       = int8_t;
 using WeiDataType      = int8_t;
@@ -26,7 +26,7 @@ static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecializatio
 
 template <ck::index_t NDimSpatial, typename InLayout, typename WeiLayout, typename OutLayout>
 using DeviceGroupedConvNDFwdInstance =
-    ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<
+    ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
         NDimSpatial,
         InLayout,
         WeiLayout,

example/42_groupnorm/CMakeLists.txt

-add_example_executable(example_groupnorm_sigmoid_mul_fp16 groupnorm_sigmoid_mul_fp16.cpp)
-add_example_executable(example_groupnorm_splitk_fp16 groupnorm_splitk_fp16.cpp)
-add_example_executable(example_groupnorm_swish_fp16 groupnorm_swish_fp16.cpp)

example/42_groupnorm/groupnorm_swish_fp16.cpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
-
-#include "common.hpp"
-
-constexpr int Rank         = 5;
-constexpr int NumReduceDim = 3;
-
-using XDataType              = ck::half_t;
-using GammaDataType          = ck::half_t;
-using BetaDataType           = ck::half_t;
-using YDataType              = ck::half_t;
-using SaveMeanInvStdDataType = float;
-using ComputeDataType        = float;
-using YElementOp             = ck::tensor_operation::element_wise::Swish;
-
-#define SAVE_MEAN_INV_STD
-
-using DeviceInstance =
-    ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
-                                                          GammaDataType,
-                                                          BetaDataType,
-                                                          ComputeDataType,
-                                                          YDataType,
-                                                          SaveMeanInvStdDataType,
-                                                          YElementOp,
-                                                          Rank,
-                                                          NumReduceDim,
-                                                          1024, // BlockSize
-                                                          1,    // ClusterM
-                                                          1024, // ClusterK
-                                                          1,    // SliceM
-                                                          32,   // SliceK
-                                                          1,    // SrcVecDim (0=M, 1=K)
-                                                          2,    // SrcScalarPerVector
-                                                          1,    // GammaVecDim (0=M, 1=K)
-                                                          2,    // GammaScalarPerVector
-                                                          1,    // BetaVecDim (0=M, 1=K)
-                                                          2,    // BetaScalarPerVector
-                                                          2,    // YScalarPerVector
-                                                          1>;   // SaveMeanInvStdScalarPerVector
-
-#include "run_groupnorm_example.inc"
-
-int main(int argc, char* argv[]) { run_groupnorm_example(argc, argv); }

example/42_groupnorm_fwd/CMakeLists.txt

+add_example_executable(example_groupnorm_fwd_sigmoid_mul_fp16 groupnorm_fwd_sigmoid_mul_fp16.cpp)
+add_example_executable(example_groupnorm_fwd_splitk_fp16 groupnorm_fwd_splitk_fp16.cpp)
+add_example_executable(example_groupnorm_fwd_swish_fp16 groupnorm_fwd_swish_fp16.cpp)

example/42_groupnorm/common.hpp → example/42_groupnorm_fwd/common.hpp

@@ -11,8 +11,8 @@
 
 #include "ck/ck.hpp"
 #include "ck/utility/reduction_enums.hpp"
-#include "ck/tensor_operation/gpu/device/impl/device_normalization_impl.hpp"
-#include "ck/tensor_operation/gpu/device/impl/device_normalization_splitk_impl.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_normalization_fwd_impl.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_normalization_fwd_splitk_impl.hpp"
 #include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
 
 #include "ck/library/utility/fill.hpp"

example/42_groupnorm/groupnorm_sigmoid_mul_fp16.cpp → example/42_groupnorm_fwd/groupnorm_fwd_sigmoid_mul_fp16.cpp

@@ -37,7 +37,7 @@ struct YElementOp
 };
 
 using DeviceInstance =
-    ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
+    ck::tensor_operation::device::DeviceNormalizationFwdImpl<XDataType,
                                                              GammaDataType,
                                                              BetaDataType,
                                                              ComputeDataType,
@@ -60,6 +60,6 @@ using DeviceInstance =
                                                              2,    // YScalarPerVector
                                                              1>;   // SaveMeanInvStdScalarPerVector
 
-#include "run_groupnorm_example.inc"
+#include "run_groupnorm_fwd_example.inc"
 
-int main(int argc, char* argv[]) { run_groupnorm_example(argc, argv); }
+int main(int argc, char* argv[]) { run_groupnorm_fwd_example(argc, argv); }

example/42_groupnorm_fwd/groupnorm_fwd_splitk_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+constexpr int Rank         = 5;
+constexpr int NumReduceDim = 3;
+
+using XDataType              = ck::half_t;
+using GammaDataType          = ck::half_t;
+using BetaDataType           = ck::half_t;
+using YDataType              = ck::half_t;
+using SaveMeanInvStdDataType = float;
+using ComputeDataType        = float;
+using YElementOp             = ck::tensor_operation::element_wise::Swish;
+
+#define SAVE_MEAN_INV_STD
+
+using DeviceInstance = ck::tensor_operation::device::DeviceNormalizationFwdSplitKImpl<
+    XDataType,
+    GammaDataType,
+    BetaDataType,
+    ComputeDataType,
+    YDataType,
+    SaveMeanInvStdDataType,
+    YElementOp,
+    Rank,
+    NumReduceDim,
+    256, // BlockSize
+    1,   // ClusterM
+    256, // ClusterK
+    1,   // SliceM
+    16,  // SliceK
+    1,   // SrcVecDim (0=M, 1=K)
+    2,   // SrcScalarPerVector
+    1,   // GammaVecDim (0=M, 1=K)
+    2,   // GammaScalarPerVector
+    1,   // BetaVecDim (0=M, 1=K)
+    2,   // BetaScalarPerVector
+    2,   // YScalarPerVector
+    1>;  // SaveMeanInvStdScalarPerVector
+
+#include "run_groupnorm_fwd_example.inc"
+
+int main(int argc, char* argv[]) { run_groupnorm_fwd_example(argc, argv); }

example/42_groupnorm_fwd/groupnorm_fwd_swish_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+constexpr int Rank         = 5;
+constexpr int NumReduceDim = 3;
+
+using XDataType              = ck::half_t;
+using GammaDataType          = ck::half_t;
+using BetaDataType           = ck::half_t;
+using YDataType              = ck::half_t;
+using SaveMeanInvStdDataType = float;
+using ComputeDataType        = float;
+using YElementOp             = ck::tensor_operation::element_wise::Swish;
+
+#define SAVE_MEAN_INV_STD
+
+using DeviceInstance =
+    ck::tensor_operation::device::DeviceNormalizationFwdImpl<XDataType,
+                                                             GammaDataType,
+                                                             BetaDataType,
+                                                             ComputeDataType,
+                                                             YDataType,
+                                                             SaveMeanInvStdDataType,
+                                                             YElementOp,
+                                                             Rank,
+                                                             NumReduceDim,
+                                                             1024, // BlockSize
+                                                             1,    // ClusterM
+                                                             1024, // ClusterK
+                                                             1,    // SliceM
+                                                             32,   // SliceK
+                                                             1,    // SrcVecDim (0=M, 1=K)
+                                                             2,    // SrcScalarPerVector
+                                                             1,    // GammaVecDim (0=M, 1=K)
+                                                             2,    // GammaScalarPerVector
+                                                             1,    // BetaVecDim (0=M, 1=K)
+                                                             2,    // BetaScalarPerVector
+                                                             2,    // YScalarPerVector
+                                                             1>;   // SaveMeanInvStdScalarPerVector
+
+#include "run_groupnorm_fwd_example.inc"
+
+int main(int argc, char* argv[]) { run_groupnorm_fwd_example(argc, argv); }

example/42_groupnorm/run_groupnorm_example.inc → example/42_groupnorm_fwd/run_groupnorm_fwd_example.inc

@@ -3,7 +3,7 @@
 
 #pragma once
 
-int run_groupnorm_example(int argc, char* argv[])
+int run_groupnorm_fwd_example(int argc, char* argv[])
 {
     ck::index_t N = 32;
     ck::index_t H = 16;

example/44_elementwise_permute/CMakeLists.txt

 add_example_executable(example_elementwise_permute_4D_fp16 elementwise_permute_4D_fp16.cpp)
 add_example_executable(example_elementwise_permute_4D_fp16_2d elementwise_permute_4D_fp16_2d.cpp)
+add_example_executable(example_elementwise_permute_4D_fp32_row elementwise_permute_4D_fp32_row.cpp)
+add_example_executable(example_elementwise_permute_4D_fp16_row elementwise_permute_4D_fp16_row.cpp)
+add_example_executable(example_elementwise_permute_4D_fp32_col elementwise_permute_4D_fp32_col.cpp)
+add_example_executable(example_elementwise_permute_4D_fp16_col elementwise_permute_4D_fp16_col.cpp)
+add_example_executable(example_elementwise_permute elementwise_permute.cpp)
+add_example_executable(example_elementwise_permute_3d elementwise_permute_3d.cpp)

example/44_elementwise_permute/elementwise_permute.cpp

+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F16;
+using BDataType = F16;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // ElementwiseOp
+                                                        5,                    // NumDim
+                                                        8,                    // MPerThread
+                                                        ck::Sequence<1>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename Functor>
+void host_elementwise4D(HostTensorB& B_ndhwc, const HostTensorA& A_ncdhw, Functor functor)
+{
+    for(std::size_t n = 0; n < A_ncdhw.mDesc.GetLengths()[0]; ++n)
+        for(std::size_t c = 0; c < A_ncdhw.mDesc.GetLengths()[1]; ++c)
+            for(std::size_t d = 0; d < A_ncdhw.mDesc.GetLengths()[2]; ++d)
+                for(std::size_t h = 0; h < A_ncdhw.mDesc.GetLengths()[3]; ++h)
+                    for(std::size_t w = 0; w < A_ncdhw.mDesc.GetLengths()[4]; ++w)
+                    {
+                        auto a_val = A_ncdhw(n, c, d, h, w);
+                        functor(B_ndhwc(n, d, h, w, c), a_val);
+                    }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    std::vector<std::size_t> ncdhw = {16, 8, 8, 8, 8};
+    std::vector<std::size_t> ndhwc = {16, 8, 8, 8, 8};
+    Tensor<ADataType> a(ncdhw);
+    Tensor<BDataType> b(ndhwc);
+
+    a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 5> ab_lengths;
+    /**std::array<ck::index_t, 5> a_strides = {
+        static_cast<int>(ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4]),
+        static_cast<int>(ncdhw[2] * ncdhw[3] * ncdhw[4]),
+        static_cast<int>(ncdhw[3] * ncdhw[4]),
+        static_cast<int>(ncdhw[4]),
+        1};
+    std::array<ck::index_t, 5> b_strides = {
+        static_cast<int>(ndhwc[1] * ndhwc[2] * ndhwc[3] * ndhwc[4]),
+        static_cast<int>(ndhwc[2] * ndhwc[3] * ndhwc[4]),
+        1,
+        static_cast<int>(ndhwc[3] * ndhwc[4]),
+        static_cast<int>(ndhwc[4])};**/
+
+    std::array<ck::index_t, 5> a_strides = {
+        static_cast<int>(ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4]),
+        static_cast<int>(ncdhw[3] * ncdhw[4]),
+        static_cast<int>(ncdhw[4]),
+        1,
+        static_cast<int>(ncdhw[2] * ncdhw[3] * ncdhw[4])};
+
+    std::array<ck::index_t, 5> b_strides = {
+        static_cast<int>(ndhwc[1] * ndhwc[2] * ndhwc[3] * ndhwc[4]),
+        static_cast<int>(ndhwc[2] * ndhwc[3] * ndhwc[4]),
+        static_cast<int>(ndhwc[3] * ndhwc[4]),
+        static_cast<int>(ndhwc[4]),
+        1};
+    ck::ranges::copy(ncdhw, ab_lengths.begin());
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(
+        ab_lengths, {a_strides}, {b_strides}, input, output, PassThrough{});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (ncdhw): " << a.mDesc << std::endl;
+    std::cout << "B (ndhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * ncdhw[0] * ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4];
+
+    std::size_t num_btype =
+        sizeof(ADataType) * (ncdhw[0] * ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4]) +
+        sizeof(BDataType) * (ncdhw[0] * ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(ndhwc);
+        host_elementwise4D(host_b, a, PassThrough{});
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}

example/44_elementwise_permute/elementwise_permute_3d.cpp

+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_3d_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F16;
+using BDataType = F16;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwise3dImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                          ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                          PassThrough,          // ElementwiseOp
+                                                          2,                    // NumDim_m, {N, C}
+                                                          2,                    // NumDim_n, {H, W}
+                                                          1,                    // NumDim_k, {D}
+                                                          8,                    // MPerThread
+                                                          8,                    // NPerThread
+                                                          8,                    // KPerThread
+                                                          ck::Sequence<8>,  // InScalarPerVectorSeq
+                                                          ck::Sequence<4>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename Functor>
+void host_elementwise4D(HostTensorB& B_ndhwc, const HostTensorA& A_ncdhw, Functor functor)
+{
+    for(std::size_t n = 0; n < A_ncdhw.mDesc.GetLengths()[0]; ++n)
+        for(std::size_t c = 0; c < A_ncdhw.mDesc.GetLengths()[1]; ++c)
+            for(std::size_t d = 0; d < A_ncdhw.mDesc.GetLengths()[2]; ++d)
+                for(std::size_t h = 0; h < A_ncdhw.mDesc.GetLengths()[3]; ++h)
+                    for(std::size_t w = 0; w < A_ncdhw.mDesc.GetLengths()[4]; ++w)
+                    {
+                        auto a_val = A_ncdhw(n, c, d, h, w);
+                        functor(B_ndhwc(n, d, h, w, c), a_val);
+                    }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    const int N = 4;
+    const int C = 16;
+    const int H = 32;
+    const int W = 5;
+    const int D = 16;
+
+    std::vector<std::size_t> ncdhw = {N, C, D, H, W};
+    std::vector<std::size_t> ndhwc = {N, D, H, W, C};
+    Tensor<ADataType> a(ncdhw);
+    Tensor<BDataType> b(ndhwc);
+
+    a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 5> ab_lengths{N, C, H, W, D};
+    std::array<ck::index_t, 5> a_strides = {C * D * H * W, H * W, W, 1, D * H * W}; // N, C, D, H, W
+    std::array<ck::index_t, 5> b_strides = {C * H * W * D, H * W * D, W * D, D, 1}; // N, D, H, W, C
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(
+        ab_lengths, {a_strides}, {b_strides}, input, output, PassThrough{});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (ncdhw): " << a.mDesc << std::endl;
+    std::cout << "B (ndhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * ncdhw[0] * ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4];
+
+    std::size_t num_btype =
+        sizeof(ADataType) * (ncdhw[0] * ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4]) +
+        sizeof(BDataType) * (ncdhw[0] * ncdhw[1] * ncdhw[2] * ncdhw[3] * ncdhw[4]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(ndhwc);
+        host_elementwise4D(host_b, a, PassThrough{});
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}

example/44_elementwise_permute/elementwise_permute_4D_fp16.cpp

@@ -19,13 +19,13 @@ using BDataType = F16;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using DeviceElementwisePermuteInstance =
-    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>,
-                                                        ck::Tuple<BDataType>,
-                                                        PassThrough,
-                                                        4,
-                                                        8,
-                                                        ck::Sequence<8>,
-                                                        ck::Sequence<1>>;
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // Elementwise op
+                                                        4,                    // NumDim
+                                                        8,                    // MPerThread
+                                                        ck::Sequence<8>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
 
 template <typename HostTensorA, typename HostTensorB, typename Functor>
 void host_elementwise4D(HostTensorB& B_nhwc, const HostTensorA& A_nchw, Functor functor)
@@ -99,7 +99,6 @@ int main()
 
     std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
               << std::endl;
-
     bool pass = true;
 
     if(do_verification)

example/44_elementwise_permute/elementwise_permute_4D_fp16_2d.cpp

@@ -17,15 +17,15 @@ using BDataType = F16;
 
 using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 using DeviceElementwisePermuteInstance =
-    ck::tensor_operation::device::DeviceElementwise2dImpl<ck::Tuple<ADataType>,
-                                                          ck::Tuple<BDataType>,
-                                                          PassThrough,
+    ck::tensor_operation::device::DeviceElementwise2dImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                          ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                          PassThrough,          // Elementwise op
                                                           3,                    // NumDim_M
                                                           1,                    // NumDim_N
-                                                          8,
-                                                          8,
-                                                          ck::Sequence<8>,
-                                                          ck::Sequence<8>>;
+                                                          1,                    // MPerThread
+                                                          1,                    // NPerThread
+                                                          ck::Sequence<1>,  // InScalarPerVectorSeq
+                                                          ck::Sequence<1>>; // OutScalarPerVectorSeq
 
 template <typename HostTensorA, typename HostTensorB, typename Functor>
 void host_elementwise4D(HostTensorB& B_nhwc,
@@ -53,12 +53,6 @@ int main()
     const int H = 32;
     const int W = 1024;
 
-    /**const int N = 120;
-    const int H = 32;
-    const int W = 64;
-
-    const int C = 128;**/
-
     std::vector<std::size_t> nchw = {N, C, H, W};
     std::vector<std::size_t> nhwc = {N, H, W, C};
 
@@ -71,7 +65,6 @@ int main()
     DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
 
     a_device_buf.ToDevice(a.mData.data());
-    // LogRangeAsType<float>(std::cout << "Tensor a  : ", a.mData, ",") << std::endl;
 
     std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
     std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
@@ -115,13 +108,10 @@ int main()
     if(do_verification)
     {
         b_device_buf.FromDevice(b.mData.data());
-        // LogRangeAsType<float>(std::cout << "Tensor b  : ", b.mData, ",") << std::endl;
 
         Tensor<BDataType> host_b(nhwc);
         host_elementwise4D<Tensor<ADataType>, Tensor<BDataType>, PassThrough>(
             host_b, a, nchw, PassThrough{});
-
-        // LogRangeAsType<float>(std::cout << "Host b  : ", host_b.mData, ",") << std::endl;
         pass &=
             ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
     }

example/44_elementwise_permute/elementwise_permute_4D_fp16_col.cpp

+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F16;
+using BDataType = F16;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using UnaryOp     = ck::tensor_operation::element_wise::UnarySquare;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // ElementwiseOp
+                                                        UnaryOp,              // UnaryOp
+                                                        Scale,                // Scalar
+                                                        4,                    // NumDim
+                                                        8,                    // MPerThread
+                                                        ck::Sequence<1>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
+void host_elementwise4D(HostTensorB& B_nhwc,
+                        const HostTensorA& A_nchw,
+                        FunctorA functor_a,
+                        FunctorB functor_b,
+                        float scale)
+{
+    std::size_t N = A_nchw.mDesc.GetLengths()[0];
+    std::size_t C = A_nchw.mDesc.GetLengths()[1];
+    std::size_t H = A_nchw.mDesc.GetLengths()[2];
+    std::size_t W = A_nchw.mDesc.GetLengths()[3];
+    for(std::size_t w = 0; w < W; ++w)
+        for(std::size_t h = 0; h < H; ++h)
+            for(std::size_t c = 0; c < C; ++c)
+                for(std::size_t n = 0; n < N; ++n)
+                {
+                    ADataType tmp_val;
+                    // auto a_val = A_nchw(n, c, h, w);
+                    auto a_val = A_nchw.mData[(n) + (c * N) + (h * C * N) + (w * H * C * N)];
+                    functor_b(tmp_val, a_val);
+                    // functor_a(B_nhwc(n, h, w, c), scale * tmp_val);
+                    functor_a(B_nhwc.mData[(n) + (c * W * H * N) + (h * N) + (w * H * N)],
+                              scale * tmp_val);
+                }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    std::vector<std::size_t> nchw = {4, 2, 1, 8};
+    std::vector<std::size_t> nhwc = {4, 1, 8, 2};
+    Tensor<ADataType> a(nchw);
+    Tensor<BDataType> b(nhwc);
+    float scale = 1.f;
+    auto i      = 0;
+    for(std::size_t w = 0; w < a.mDesc.GetLengths()[3]; ++w)
+        for(std::size_t h = 0; h < a.mDesc.GetLengths()[2]; ++h)
+            for(std::size_t c = 0; c < a.mDesc.GetLengths()[1]; ++c)
+                for(std::size_t n = 0; n < a.mDesc.GetLengths()[0]; ++n)
+                {
+                    a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
+                            (h * nchw[3]) + w] = i;
+                    i++;
+                }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 4> ab_lengths;
+
+    std::array<ck::index_t, 4> a_strides = {1,
+                                            static_cast<int>(nchw[0]),
+                                            static_cast<int>(nchw[0] * nchw[1]),
+                                            static_cast<int>(nchw[0] * nchw[1] * nchw[2])};
+
+    std::array<ck::index_t, 4> b_strides = {1,
+                                            static_cast<int>(nhwc[0] * nhwc[1] * nhwc[2]),
+                                            static_cast<int>(nhwc[0]),
+                                            static_cast<int>(nhwc[0] * nhwc[1])};
+    ck::ranges::copy(nchw, ab_lengths.begin());
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(ab_lengths,
+                                                         {a_strides},
+                                                         {b_strides},
+                                                         input,
+                                                         output,
+                                                         PassThrough{},
+                                                         UnaryOp{},
+                                                         Scale{scale});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (nchw): " << a.mDesc << std::endl;
+    std::cout << "B (nhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
+
+    std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
+                            sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(nhwc);
+        host_elementwise4D(host_b, a, PassThrough{}, UnaryOp{}, scale);
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}

example/44_elementwise_permute/elementwise_permute_4D_fp16_row.cpp

+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F16;
+using BDataType = F16;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using UnaryOp     = ck::tensor_operation::element_wise::UnarySquare;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // ElementwiseOp
+                                                        UnaryOp,              // UnaryOp
+                                                        Scale,                // Scalar
+                                                        4,                    // NumDim
+                                                        8,                    // MPerThread
+                                                        ck::Sequence<8>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
+void host_elementwise4D(HostTensorB& B_nhwc,
+                        const HostTensorA& A_nchw,
+                        FunctorA functor_a,
+                        FunctorB functor_b,
+                        float scale)
+{
+    for(std::size_t n = 0; n < A_nchw.mDesc.GetLengths()[0]; ++n)
+        for(std::size_t c = 0; c < A_nchw.mDesc.GetLengths()[1]; ++c)
+            for(std::size_t h = 0; h < A_nchw.mDesc.GetLengths()[2]; ++h)
+                for(std::size_t w = 0; w < A_nchw.mDesc.GetLengths()[3]; ++w)
+                {
+                    ADataType tmp_val;
+                    auto a_val = A_nchw(n, c, h, w);
+                    functor_b(tmp_val, a_val);
+                    functor_a(B_nhwc(n, h, w, c), scale * tmp_val);
+                }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    std::vector<std::size_t> nchw = {16, 128, 32, 64};
+    std::vector<std::size_t> nhwc = {16, 32, 64, 128};
+    Tensor<ADataType> a(nchw);
+    Tensor<BDataType> b(nhwc);
+    float scale = 2.f;
+    a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 4> ab_lengths;
+    std::array<ck::index_t, 4> a_strides = {static_cast<int>(nchw[1] * nchw[2] * nchw[3]),
+                                            static_cast<int>(nchw[2] * nchw[3]),
+                                            static_cast<int>(nchw[3]),
+                                            1};
+    std::array<ck::index_t, 4> b_strides = {static_cast<int>(nhwc[1] * nhwc[2] * nhwc[3]),
+                                            1,
+                                            static_cast<int>(nhwc[2] * nhwc[3]),
+                                            static_cast<int>(nhwc[3])};
+
+    ck::ranges::copy(nchw, ab_lengths.begin());
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(ab_lengths,
+                                                         {a_strides},
+                                                         {b_strides},
+                                                         input,
+                                                         output,
+                                                         PassThrough{},
+                                                         UnaryOp{},
+                                                         Scale{scale});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (nchw): " << a.mDesc << std::endl;
+    std::cout << "B (nhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
+
+    std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
+                            sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(nhwc);
+        host_elementwise4D(host_b, a, PassThrough{}, UnaryOp{}, scale);
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}

example/44_elementwise_permute/elementwise_permute_4D_fp32_col.cpp

+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F32;
+using BDataType = F32;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using UnaryOp     = ck::tensor_operation::element_wise::UnarySquare;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // ElementwiseOp
+                                                        UnaryOp,              // UnaryOp
+                                                        Scale,                // Scalar
+                                                        4,                    // NumDim
+                                                        1,                    // MPerThread
+                                                        ck::Sequence<1>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
+void host_elementwise4D(HostTensorB& B_nhwc,
+                        const HostTensorA& A_nchw,
+                        FunctorA functor_a,
+                        FunctorB functor_b,
+                        float scale)
+{
+    std::size_t N = A_nchw.mDesc.GetLengths()[0];
+    std::size_t C = A_nchw.mDesc.GetLengths()[1];
+    std::size_t H = A_nchw.mDesc.GetLengths()[2];
+    std::size_t W = A_nchw.mDesc.GetLengths()[3];
+    for(std::size_t w = 0; w < W; ++w)
+        for(std::size_t h = 0; h < H; ++h)
+            for(std::size_t c = 0; c < C; ++c)
+                for(std::size_t n = 0; n < N; ++n)
+                {
+                    ADataType tmp_val;
+                    auto a_val = A_nchw.mData[(n) + (c * N) + (h * C * N) + (w * H * C * N)];
+                    functor_b(tmp_val, a_val);
+                    functor_a(B_nhwc.mData[(n) + (c * W * H * N) + (h * N) + (w * H * N)],
+                              scale * tmp_val);
+                }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    std::vector<std::size_t> nchw = {5, 4, 2, 3};
+    std::vector<std::size_t> nhwc = {5, 2, 3, 4};
+    Tensor<ADataType> a(nchw);
+    Tensor<BDataType> b(nhwc);
+
+    float scale = 1.f;
+    auto i      = 0;
+    for(std::size_t w = 0; w < a.mDesc.GetLengths()[3]; ++w)
+        for(std::size_t h = 0; h < a.mDesc.GetLengths()[2]; ++h)
+            for(std::size_t c = 0; c < a.mDesc.GetLengths()[1]; ++c)
+                for(std::size_t n = 0; n < a.mDesc.GetLengths()[0]; ++n)
+                {
+                    a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
+                            (h * nchw[3]) + w] = i;
+                    i++;
+                }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 4> ab_lengths;
+
+    std::array<ck::index_t, 4> a_strides = {1,
+                                            static_cast<int>(nchw[0]),
+                                            static_cast<int>(nchw[0] * nchw[1]),
+                                            static_cast<int>(nchw[0] * nchw[1] * nchw[2])};
+
+    std::array<ck::index_t, 4> b_strides = {1,
+                                            static_cast<int>(nhwc[0] * nhwc[1] * nhwc[2]),
+                                            static_cast<int>(nhwc[0]),
+                                            static_cast<int>(nhwc[0] * nhwc[1])};
+    ck::ranges::copy(nchw, ab_lengths.begin());
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(ab_lengths,
+                                                         {a_strides},
+                                                         {b_strides},
+                                                         input,
+                                                         output,
+                                                         PassThrough{},
+                                                         UnaryOp{},
+                                                         Scale{scale});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (nchw): " << a.mDesc << std::endl;
+    std::cout << "B (nhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
+
+    std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
+                            sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(nhwc);
+        host_elementwise4D(host_b, a, PassThrough{}, UnaryOp{}, scale);
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}

example/44_elementwise_permute/elementwise_permute_4D_fp32_row.cpp

+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F32;
+using BDataType = F32;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using UnaryOp     = ck::tensor_operation::element_wise::UnarySquare;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // ElementwiseOp
+                                                        UnaryOp,              // UnaryOp
+                                                        Scale,                // Scalar
+                                                        4,                    // NumDim
+                                                        8,                    // MPerThread
+                                                        ck::Sequence<8>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
+void host_elementwise4D(HostTensorB& B_nhwc,
+                        const HostTensorA& A_nchw,
+                        FunctorA functor_a,
+                        FunctorB functor_b,
+                        float scale)
+{
+    for(std::size_t n = 0; n < A_nchw.mDesc.GetLengths()[0]; ++n)
+        for(std::size_t c = 0; c < A_nchw.mDesc.GetLengths()[1]; ++c)
+            for(std::size_t h = 0; h < A_nchw.mDesc.GetLengths()[2]; ++h)
+                for(std::size_t w = 0; w < A_nchw.mDesc.GetLengths()[3]; ++w)
+                {
+                    ADataType tmp_val;
+                    auto a_val = A_nchw(n, c, h, w);
+                    functor_b(tmp_val, a_val);
+                    functor_a(B_nhwc(n, h, w, c), scale * tmp_val);
+                }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    std::vector<std::size_t> nchw = {16, 128, 32, 64};
+    std::vector<std::size_t> nhwc = {16, 32, 64, 128};
+    Tensor<ADataType> a(nchw);
+    Tensor<BDataType> b(nhwc);
+    float scale = 2.f;
+    a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 4> ab_lengths;
+    std::array<ck::index_t, 4> a_strides = {static_cast<int>(nchw[1] * nchw[2] * nchw[3]),
+                                            static_cast<int>(nchw[2] * nchw[3]),
+                                            static_cast<int>(nchw[3]),
+                                            1};
+    std::array<ck::index_t, 4> b_strides = {static_cast<int>(nhwc[1] * nhwc[2] * nhwc[3]),
+                                            1,
+                                            static_cast<int>(nhwc[2] * nhwc[3]),
+                                            static_cast<int>(nhwc[3])};
+
+    ck::ranges::copy(nchw, ab_lengths.begin());
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(ab_lengths,
+                                                         {a_strides},
+                                                         {b_strides},
+                                                         input,
+                                                         output,
+                                                         PassThrough{},
+                                                         UnaryOp{},
+                                                         Scale{scale});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (nchw): " << a.mDesc << std::endl;
+    std::cout << "B (nhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
+
+    std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
+                            sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(nhwc);
+        host_elementwise4D(host_b, a, PassThrough{}, UnaryOp{}, scale);
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}

example/53_layernorm_bwd/CMakeLists.txt

+add_example_executable(example_layernorm2d_bwd_fp16 layernorm2d_bwd_fp16.cpp)

example/53_layernorm_bwd/layernorm2d_bwd_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <getopt.h>
+
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_common_util.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_normalization_bwd_gamma_beta_impl.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_layernorm_bwd.hpp"
+
+using DYDataType         = ck::half_t;
+using XDataType          = ck::half_t;
+using GammaDataType      = ck::half_t;
+using MeanInvStdDataType = float;
+using DGammaDataType     = ck::half_t;
+using DBetaDataType      = ck::half_t;
+using DXDataType         = ck::half_t;
+using ComputeDataType    = float;
+
+constexpr int Rank         = 2;
+constexpr int NumReduceDim = 1;
+
+// Layernorm:
+
+// Input shape
+// dy:        [M, N]
+// x:         [M, N]
+// mean:      [M, 1]
+// inv_std:   [M, 1]
+
+// Output shape
+// dgamma: [1, N]
+// dbeta:  [1, N]
+
+// dgamma = reduce_sum(dy * (x - mean) * inv_std, axis=0)
+// dbeta = reduce_sum(dy, axis=0)
+
+// [CAUSION]
+// In DeviceNormalizationBwdGammaBetaImpl, M is invarient dimension, K is reduced dimension
+// Hence, M in this example and DeviceNormalizationBwdGammaBetaImpl is different
+using GammaBetaDeviceInstance = ck::tensor_operation::device::DeviceNormalizationBwdGammaBetaImpl<
+    DYDataType,
+    XDataType,
+    MeanInvStdDataType,
+    ComputeDataType,
+    DGammaDataType,
+    DBetaDataType,
+    Rank,
+    NumReduceDim,
+    256,   // BlockSize
+    8,     // ClusterInvarient
+    32,    // ClusterReduce
+    8,     // SliceInvarient
+    1,     // SliceReduce
+    false, // IsDYFastestDimReduced
+    8,     // DYSrcVectorSize
+    false, // IsXFastestDimReduced
+    8,     // XSrcVectorSize
+    true,  // IsMeanInvStdFastestDimReduced
+    1,     // MeanInvStdSrcVectorSize
+    1,     // DGammaDstVectorSize
+    1>;    // DBetaDstVectorSize
+
+int main()
+{
+    bool time_kernel = false;
+
+    ck::index_t M = 1024;
+    ck::index_t N = 512;
+
+    Tensor<DYDataType> dy({M, N});
+    Tensor<XDataType> x({M, N});
+    Tensor<GammaDataType> gamma({N});
+    Tensor<MeanInvStdDataType> mean({M});
+    Tensor<MeanInvStdDataType> inv_std({M});
+
+    Tensor<DGammaDataType> dgamma({N});
+    Tensor<DBetaDataType> dbeta({N});
+    Tensor<DXDataType> dx({M, N});
+
+    dy.GenerateTensorValue(GeneratorTensor_3<DYDataType>{0.0, 1.0});
+    x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0.0, 1.0});
+    gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
+    mean.GenerateTensorValue(GeneratorTensor_3<MeanInvStdDataType>{0.0, 1.0});
+    inv_std.GenerateTensorValue(GeneratorTensor_3<MeanInvStdDataType>{0.0, 1.0});
+
+    DeviceMem dy_dev(sizeof(DYDataType) * dy.mDesc.GetElementSpaceSize());
+    DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
+    DeviceMem mean_dev(sizeof(MeanInvStdDataType) * mean.mDesc.GetElementSpaceSize());
+    DeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * inv_std.mDesc.GetElementSpaceSize());
+    DeviceMem dgamma_dev(sizeof(DGammaDataType) * dgamma.mDesc.GetElementSpaceSize());
+    DeviceMem dbeta_dev(sizeof(DBetaDataType) * dbeta.mDesc.GetElementSpaceSize());
+
+    dy_dev.ToDevice(dy.mData.data());
+    x_dev.ToDevice(x.mData.data());
+    mean_dev.ToDevice(mean.mData.data());
+    inv_std_dev.ToDevice(inv_std.mData.data());
+
+    auto gamma_beta_device_instance = GammaBetaDeviceInstance{};
+    auto gamma_beta_argument_ptr =
+        gamma_beta_device_instance.MakeArgumentPointer({M, N}, // inLengths
+                                                       {N, 1}, // dyStrides
+                                                       {N, 1}, // xStrides
+                                                       {1, 0}, // meanStrides
+                                                       {1, 0}, // invStdStrides
+                                                       {N},    // outLengths
+                                                       {1},    // dgammaStrides
+                                                       {1},    // dbetaStrides
+                                                       {0},    // reduceDims
+                                                       dy_dev.GetDeviceBuffer(),
+                                                       x_dev.GetDeviceBuffer(),
+                                                       mean_dev.GetDeviceBuffer(),
+                                                       inv_std_dev.GetDeviceBuffer(),
+                                                       dgamma_dev.GetDeviceBuffer(),
+                                                       dbeta_dev.GetDeviceBuffer());
+
+    if(!gamma_beta_device_instance.IsSupportedArgument(gamma_beta_argument_ptr.get()))
+    {
+        std::cout << "The runtime parameters are not supported" << std::endl;
+        return 1;
+    };
+
+    auto gamma_beta_invoker_ptr = gamma_beta_device_instance.MakeInvokerPointer();
+    gamma_beta_invoker_ptr->Run(gamma_beta_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
+
+    bool pass = true;
+    {
+        Tensor<DGammaDataType> host_dgamma({N});
+        Tensor<DBetaDataType> host_dbeta({N});
+        Tensor<DXDataType> host_dx({M, N});
+        using ReferenceInstance =
+            ck::tensor_operation::host::ReferenceLayernormBwd<DYDataType,
+                                                              XDataType,
+                                                              GammaDataType,
+                                                              MeanInvStdDataType,
+                                                              DGammaDataType,
+                                                              DBetaDataType,
+                                                              DXDataType,
+                                                              ComputeDataType>;
+
+        ReferenceInstance ref;
+        auto ref_argument =
+            ref.MakeArgument(dy, x, gamma, mean, inv_std, host_dgamma, host_dbeta, host_dx, {M, N});
+        auto ref_invoker = ref.MakeInvoker();
+        ref_invoker.Run(ref_argument);
+
+        dgamma_dev.FromDevice(dgamma.mData.data());
+        dbeta_dev.FromDevice(dbeta.mData.data());
+
+        pass &= ck::utils::check_err(dgamma, host_dgamma, "Error: Incorrect dgamma", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(dbeta, host_dbeta, "Error: Incorrect dbeta", 1e-3, 1e-3);
+    }
+
+    return (pass ? 0 : 1);
+}