Merge remote-tracking branch 'upstream/develop' into merge_upstream_1129

also fix regression

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;
@@ -34,6 +34,8 @@ int run_groupnorm_example(int argc, char* argv[])
     Tensor<YDataType> y({N, H, W, G, C});
     Tensor<GammaDataType> gamma({G, C});
     Tensor<BetaDataType> beta({G, C});
+    Tensor<SaveMeanInvStdDataType> save_mean({N, G});
+    Tensor<SaveMeanInvStdDataType> save_inv_std({N, G});
 
     ck::utils::FillUniformDistribution<XDataType>{0.f, 1.f}(x);
     ck::utils::FillUniformDistribution<GammaDataType>{0.f, 1.f}(gamma);
@@ -43,6 +45,11 @@ int run_groupnorm_example(int argc, char* argv[])
     DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
     DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
     DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
+#ifdef SAVE_MEAN_INV_STD
+    DeviceMem save_mean_dev(sizeof(SaveMeanInvStdDataType) * save_mean.mDesc.GetElementSpaceSize());
+    DeviceMem save_inv_std_dev(sizeof(SaveMeanInvStdDataType) *
+                               save_inv_std.mDesc.GetElementSpaceSize());
+#endif
 
     x_dev.ToDevice(x.mData.data());
     gamma_dev.ToDevice(gamma.mData.data());
@@ -57,14 +64,23 @@ int run_groupnorm_example(int argc, char* argv[])
         {0, 0, 0, C, 1},
         {0, 0, 0, C, 1},
         std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
+        std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
+                                 save_mean.mDesc.GetStrides().end()},
+        std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
+                                 save_mean.mDesc.GetStrides().end()},
         {1, 2, 4}, // reduction dimension: [H, W, C]
         1e-6,
         x_dev.GetDeviceBuffer(),
         gamma_dev.GetDeviceBuffer(),
         beta_dev.GetDeviceBuffer(),
         y_dev.GetDeviceBuffer(),
+#ifdef SAVE_MEAN_INV_STD
+        save_mean_dev.GetDeviceBuffer(),
+        save_inv_std_dev.GetDeviceBuffer(),
+#else
         nullptr,
         nullptr,
+#endif
         y_element_op);
 
     if(!device_instance.IsSupportedArgument(argument_ptr.get()))
@@ -92,21 +108,40 @@ int run_groupnorm_example(int argc, char* argv[])
     bool pass = true;
     {
         Tensor<YDataType> host_y({N, H, W, G, C});
-        using ReferenceInstance = ck::tensor_operation::host::ReferenceGroupnorm<XDataType,
-                                                                                 GammaDataType,
-                                                                                 BetaDataType,
-                                                                                 YDataType,
-                                                                                 ComputeDataType,
-                                                                                 YElementOp>;
+        Tensor<SaveMeanInvStdDataType> host_save_mean(HostTensorDescriptor{N, G});
+        Tensor<SaveMeanInvStdDataType> host_save_inv_std(HostTensorDescriptor{N, G});
+        using ReferenceInstance =
+            ck::tensor_operation::host::ReferenceGroupnorm<XDataType,
+                                                           GammaDataType,
+                                                           BetaDataType,
+                                                           YDataType,
+                                                           SaveMeanInvStdDataType,
+                                                           ComputeDataType,
+                                                           YElementOp>;
 
         ReferenceInstance ref;
-        auto ref_argument =
-            ref.MakeArgument(x, gamma, beta, host_y, y_element_op, {N, H, W, G, C}, 1e-6);
-        auto ref_invoker = ref.MakeInvoker();
+        auto ref_argument = ref.MakeArgument(x,
+                                             gamma,
+                                             beta,
+                                             host_y,
+                                             host_save_mean,
+                                             host_save_inv_std,
+                                             y_element_op,
+                                             {N, H, W, G, C},
+                                             1e-6);
+        auto ref_invoker  = ref.MakeInvoker();
         ref_invoker.Run(ref_argument);
 
         y_dev.FromDevice(y.mData.data());
         pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3);
+#ifdef SAVE_MEAN_INV_STD
+        save_mean_dev.FromDevice(save_mean.mData.data());
+        save_inv_std_dev.FromDevice(save_inv_std.mData.data());
+        pass &= ck::utils::check_err(
+            save_mean, host_save_mean, "Error: Incorrect results (mean)", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(
+            save_inv_std, host_save_inv_std, "Error: Incorrect results (inv_std)", 1e-3, 1e-3);
+#endif
     }
 
     return (pass ? 0 : 1);

example/43_splitk_gemm_bias_e_permute/CMakeLists.txt

-if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-    add_example_executable(example_splitk_gemm_bias_e_permute_xdl_fp16 splitk_gemm_bias_e_permute_xdl_fp16.cpp)
-endif()
-if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
-    add_example_executable(example_splitk_gemm_bias_e_permute_xdl_fp32 splitk_gemm_bias_e_permute_xdl_fp32.cpp)
-endif()
+add_example_executable(example_splitk_gemm_bias_e_permute_xdl_fp16 splitk_gemm_bias_e_permute_xdl_fp16.cpp)
+add_example_executable(example_splitk_gemm_bias_e_permute_xdl_fp32 splitk_gemm_bias_e_permute_xdl_fp32.cpp)

example/44_elementwise_permute/CMakeLists.txt

-if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-    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_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)
+if((NOT GPU_TARGETS MATCHES "gfx940") AND (NOT GPU_TARGETS MATCHES "gfx941") AND (NOT GPU_TARGETS MATCHES "gfx942"))
+    add_example_executable(example_elementwise_permute_3d elementwise_permute_3d.cpp)
 endif()

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,
-                                                          3, // NumDim_M
-                                                          1, // NumDim_N
-                                                          8,
-                                                          8,
-                                                          ck::Sequence<8>,
-                                                          ck::Sequence<8>>;
+    ck::tensor_operation::device::DeviceElementwise2dImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                          ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                          PassThrough,          // Elementwise op
+                                                          3,                    // NumDim_M
+                                                          1,                    // NumDim_N
+                                                          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/45_elementwise_normalization/elementwise_layernorm_blockwise.cpp

@@ -167,20 +167,31 @@ int main()
                            XElementwiseOperation>(x, a, b, mn, XElementwiseOperation{});
 
         Tensor<YDataType> host_y(f_host_tensor_descriptor2d(M, N, Stride));
+        Tensor<AccDataType> host_save_mean({M});
+        Tensor<AccDataType> host_save_inv_std({M});
         using ReferenceInstance =
             ck::tensor_operation::host::ReferenceLayernorm<XDataType,
                                                            GammaDataType,
                                                            BetaDataType,
                                                            YDataType,
                                                            AccDataType,
+                                                           AccDataType,
                                                            YElementwiseOperation,
                                                            Rank,
                                                            NumReduceDim>;
 
         ReferenceInstance ref;
-        auto ref_argument =
-            ref.MakeArgument(x, gamma, beta, host_y, YElementwiseOperation{}, {M, N}, {1}, 1e-4);
-        auto ref_invoker = ref.MakeInvoker();
+        auto ref_argument = ref.MakeArgument(x,
+                                             gamma,
+                                             beta,
+                                             host_y,
+                                             host_save_mean,
+                                             host_save_inv_std,
+                                             YElementwiseOperation{},
+                                             {M, N},
+                                             {1},
+                                             1e-4);
+        auto ref_invoker  = ref.MakeInvoker();
         ref_invoker.Run(ref_argument);
 
         y_dev.FromDevice(y.mData.data());

example/46_gemm_add_multiply/CMakeLists.txt

-if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-    if(DL_KERNELS)
-        add_example_executable(example_gemm_add_multiply_dl_fp16 gemm_add_multiply_dl_fp16.cpp)
-    endif()
-    add_example_executable(example_gemm_add_multiply_xdl_fp16 gemm_add_multiply_xdl_fp16.cpp)
-endif()
+add_example_executable(example_gemm_add_multiply_dl_fp16 gemm_add_multiply_dl_fp16.cpp)
+add_example_executable(example_gemm_add_multiply_xdl_fp16 gemm_add_multiply_xdl_fp16.cpp)

example/48_pool3d_fwd/CMakeLists.txt

-if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-    add_example_executable(example_pool3d_fwd_fp16 pool3d_fwd_fp16.cpp)
-endif()
+add_example_executable(example_pool3d_fwd_fp16 pool3d_fwd_fp16.cpp)

example/49_maxpool2d_bwd/CMakeLists.txt

-if(DTYPES MATCHES "bf16" OR NOT DEFINED DTYPES)
-    add_example_executable(example_maxpool2d_bwd_bf16 maxpool2d_bwd_bf16.cpp)
-endif()
-if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-    add_example_executable(example_maxpool2d_bwd_fp16 maxpool2d_bwd_fp16.cpp)
-endif()
-if(DTYPES MATCHES "fp32" OR NOT DEFINED DTYPES)
-    add_example_executable(example_maxpool2d_bwd_fp32 maxpool2d_bwd_fp32.cpp)
-endif()
+add_example_executable(example_maxpool2d_bwd_bf16 maxpool2d_bwd_bf16.cpp)
+add_example_executable(example_maxpool2d_bwd_fp16 maxpool2d_bwd_fp16.cpp)
+add_example_executable(example_maxpool2d_bwd_fp32 maxpool2d_bwd_fp32.cpp)

example/50_put_element/CMakeLists.txt

-if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
-    add_example_executable(example_put_element_fp16 put_element_fp16.cpp)
-endif()
+add_example_executable(example_put_element_fp16 put_element_fp16.cpp)

example/52_im2col_col2im/CMakeLists.txt

+list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
+set(target 0)
+foreach(gpu IN LISTS GPU_TARGETS)
+    if(gpu IN_LIST gpu_list AND target EQUAL 0)
+        add_custom_target(example_im2col_col2im)
+
+        add_example_executable(example_image_to_column_f32 image_to_column_f32.cpp)
+        add_example_dependencies(example_im2col_col2im example_image_to_column_f32)
+
+        add_example_executable(example_column_to_image_f32 column_to_image_f32.cpp)
+        add_example_dependencies(example_im2col_col2im example_column_to_image_f32)
+
+        set(target 1)
+    endif()
+endforeach()

example/52_im2col_col2im/column_to_image_f32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+using InDataType  = FP32; // ck::bhalf_t;//FP32;
+using OutDataType = FP32; // ck::bhalf_t;//FP32;
+
+using ImLayout        = ck::tensor_layout::convolution::GNHWC;
+using ColumnToImageOp = ck::conv_tensor_rearrange_op::ColumnToImage;
+
+// clang-format off
+using DeviceColToImgInstance = ck::tensor_operation::device::DeviceColumnToImageImpl
+        //#####################|        Num| ImLayout| InDataType| OutDataType| Block|  MPer|  KPer|    Thread| Scalar|
+        //#####################|        Dim|         |           |            |  Size| Block| Block|   Cluster|    Per|
+        //#####################|    Spatial|         |           |            |      |      |      |   Lengths| Vector|
+        //#####################|           |         |           |            |      |      |      |          |       |
+                              < NDimSpatial, ImLayout, InDataType, OutDataType,   256,   128,   128, S<16, 16>,     1>;
+// clang-format on
+
+bool RunColumnToImage(const ExecutionConfig& config, const ck::utils::conv::ConvParam& conv_params)
+{
+    const auto G = conv_params.G_;
+    const auto N = conv_params.N_;
+    const auto C = conv_params.C_;
+
+    const ck::index_t NDoHoWo =
+        N * ck::accumulate_n<ck::index_t>(
+                conv_params.output_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
+    const ck::index_t CZYX =
+        C * ck::accumulate_n<ck::index_t>(
+                conv_params.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
+
+    const auto in_desc = HostTensorDescriptor({G, NDoHoWo, CZYX});
+    const auto out_desc =
+        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
+
+    std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
+    std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
+    std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
+    std::array<ck::index_t, 3> gemm_g_m_k_strides{};
+    std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
+    std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
+    std::array<ck::index_t, NDimSpatial> input_left_pads{};
+    std::array<ck::index_t, NDimSpatial> input_right_pads{};
+
+    auto copy = [](const auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
+
+    copy(conv_params.input_spatial_lengths_, input_spatial_lengths);
+    copy(conv_params.filter_spatial_lengths_, filter_spatial_lengths);
+    copy(conv_params.output_spatial_lengths_, output_spatial_lengths);
+    copy(in_desc.GetStrides(), gemm_g_m_k_strides);
+    copy(out_desc.GetStrides(), image_g_n_c_wis_strides);
+    copy(conv_params.conv_filter_strides_, conv_filter_strides);
+    copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
+    copy(conv_params.input_left_pads_, input_left_pads);
+    copy(conv_params.input_right_pads_, input_right_pads);
+
+    Tensor<InDataType> in(in_desc);
+    Tensor<OutDataType> out_device(out_desc);
+    Tensor<OutDataType> out_host(out_desc);
+
+    std::cout << "in: " << in.mDesc << std::endl;
+    std::cout << "out: " << out_device.mDesc << std::endl;
+
+    switch(config.init_method)
+    {
+    case 0: break;
+    case 1: in.GenerateTensorValue(GeneratorTensor_2<InDataType>{1, 2}); break;
+    default: in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-0.5, 0.5});
+    }
+
+    DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
+    DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
+
+    in_device_buf.ToDevice(in.mData.data());
+
+    // reset input to zero
+    out_device_buf.SetZero();
+
+    static_assert(std::is_default_constructible_v<DeviceColToImgInstance>);
+
+    // do conv
+    auto col2img  = DeviceColToImgInstance{};
+    auto invoker  = col2img.MakeInvoker();
+    auto argument = col2img.MakeArgument(in_device_buf.GetDeviceBuffer(),
+                                         out_device_buf.GetDeviceBuffer(),
+                                         G,
+                                         N,
+                                         C,
+                                         input_spatial_lengths,
+                                         filter_spatial_lengths,
+                                         output_spatial_lengths,
+                                         image_g_n_c_wis_strides,
+                                         gemm_g_m_k_strides,
+                                         conv_filter_strides,
+                                         conv_filter_dilations,
+                                         input_left_pads,
+                                         input_right_pads);
+
+    if(!col2img.IsSupportedArgument(argument))
+    {
+        std::cerr << "wrong! device_col2img with the specified compilation parameters does "
+                     "not support this col2img problem"
+                  << std::endl;
+
+        return false;
+    }
+
+    float ave_time        = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
+    std::size_t num_btype = G * NDoHoWo * CZYX * (sizeof(OutDataType) + sizeof(InDataType));
+    float gb_per_sec      = num_btype / 1.E6 / ave_time;
+    std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
+
+    if(config.do_verification)
+    {
+        auto ref_column_to_image = ck::tensor_operation::host::
+            ReferenceColumnToImage<NDimSpatial, ImLayout, InDataType, OutDataType>();
+
+        auto ref_invoker = ref_column_to_image.MakeInvoker();
+
+        auto ref_argument = ref_column_to_image.MakeArgument(in,
+                                                             out_host,
+                                                             conv_params.filter_spatial_lengths_,
+                                                             conv_params.conv_filter_strides_,
+                                                             conv_params.conv_filter_dilations_,
+                                                             conv_params.input_left_pads_,
+                                                             conv_params.input_right_pads_);
+
+        if(!ref_column_to_image.IsSupportedArgument(&ref_argument))
+        {
+            std::cerr << "wrong! ref_col2img with the specified compilation parameters does "
+                         "not support this col2img problem"
+                      << std::endl;
+            return false;
+        }
+
+        ref_invoker.Run(ref_argument);
+        out_device_buf.FromDevice(out_device.mData.data());
+        return ck::utils::check_err(out_device.mData, out_host.mData);
+    }
+
+    return true;
+}
+
+int RunColumnToImageExample(int argc, char* argv[])
+{
+    ExecutionConfig config;
+    ck::utils::conv::ConvParam conv_params = DefaultConvParams;
+
+    if(!parse_cmd_args(argc, argv, config, conv_params))
+    {
+        return EXIT_FAILURE;
+    }
+
+    if(conv_params.num_dim_spatial_ != NDimSpatial)
+    {
+        std::cerr << "unsupported # of spatial dimensions" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    return !RunColumnToImage(config, conv_params);
+}
+
+int main(int argc, char* argv[]) { return RunColumnToImageExample(argc, argv); }

example/52_image_to_column/common.hpp → example/52_im2col_col2im/common.hpp

@@ -10,6 +10,7 @@
 
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_image_to_column_impl.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_column_to_image_impl.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 
 #include "ck/library/utility/algorithm.hpp"
@@ -20,6 +21,7 @@
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_image_to_column.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_column_to_image.hpp"
 
 template <ck::index_t... Is>
 using S = ck::Sequence<Is...>;
@@ -32,7 +34,7 @@ struct ExecutionConfig final
 {
     bool do_verification = true;
     int init_method      = 1;
-    bool time_kernel     = true;
+    bool time_kernel     = false;
 };
 
 #define DefaultConvParams                                                            \

example/52_image_to_column/image_to_column_f32.cpp → example/52_im2col_col2im/image_to_column_f32.cpp

@@ -6,20 +6,21 @@
 using InDataType  = FP32;
 using OutDataType = FP32;
 
-using InLayout = ck::tensor_layout::convolution::GNHWC;
+using ImLayout        = ck::tensor_layout::convolution::GNHWC;
+using ImageToColumnOp = ck::conv_tensor_rearrange_op::ImageToColumn;
 
 // clang-format off
 using DeviceImgToColInstance = ck::tensor_operation::device::DeviceImageToColumnImpl
-        //#####################|        Num| InLayout| InDataType| OutDataType| Block|  MPer|  KPer|    Thread| Scalar|
+        //#####################|        Num| ImLayout| InDataType| OutDataType| Block|  MPer|  KPer|    Thread| Scalar|
         //#####################|        Dim|         |           |            |  Size| Block| Block|   Cluster|    Per|
         //#####################|    Spatial|         |           |            |      |      |      |   Lengths| Vector|
         //#####################|           |         |           |            |      |      |      |          |       |
-                              < NDimSpatial, InLayout, InDataType, OutDataType,   256,   128,   128, S<16, 16>,     1>;
+                              < NDimSpatial, ImLayout, InDataType, OutDataType,   256,   128,   128, S<16, 16>,      1>;
 // clang-format on
 
 bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::ConvParam& conv_params)
 {
-
+    const auto G = conv_params.G_;
     const auto N = conv_params.N_;
     const auto C = conv_params.C_;
 
@@ -31,14 +32,14 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
                 conv_params.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
 
     const auto in_desc =
-        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_params);
-    const auto out_desc = HostTensorDescriptor({NDoHoWo, CZYX});
+        ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
+    const auto out_desc = HostTensorDescriptor({G, NDoHoWo, CZYX});
 
     std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
     std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
     std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
-    std::array<ck::index_t, NDimSpatial + 3> input_g_n_c_wis_strides{};
-    std::array<ck::index_t, 2> output_m_k_strides{};
+    std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
+    std::array<ck::index_t, 3> gemm_g_m_k_strides{};
     std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
     std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
     std::array<ck::index_t, NDimSpatial> input_left_pads{};
@@ -49,8 +50,8 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
     copy(conv_params.input_spatial_lengths_, input_spatial_lengths);
     copy(conv_params.filter_spatial_lengths_, filter_spatial_lengths);
     copy(conv_params.output_spatial_lengths_, output_spatial_lengths);
-    copy(in_desc.GetStrides(), input_g_n_c_wis_strides);
-    copy(out_desc.GetStrides(), output_m_k_strides);
+    copy(in_desc.GetStrides(), image_g_n_c_wis_strides);
+    copy(out_desc.GetStrides(), gemm_g_m_k_strides);
     copy(conv_params.conv_filter_strides_, conv_filter_strides);
     copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
     copy(conv_params.input_left_pads_, input_left_pads);
@@ -85,13 +86,14 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
     auto invoker  = img2col.MakeInvoker();
     auto argument = img2col.MakeArgument(in_device_buf.GetDeviceBuffer(),
                                          out_device_buf.GetDeviceBuffer(),
+                                         G,
                                          N,
                                          C,
                                          input_spatial_lengths,
                                          filter_spatial_lengths,
                                          output_spatial_lengths,
-                                         input_g_n_c_wis_strides,
-                                         output_m_k_strides,
+                                         image_g_n_c_wis_strides,
+                                         gemm_g_m_k_strides,
                                          conv_filter_strides,
                                          conv_filter_dilations,
                                          input_left_pads,
@@ -107,14 +109,14 @@ bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::Conv
     }
 
     float ave_time        = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
-    std::size_t num_btype = NDoHoWo * CZYX * (sizeof(OutDataType) + sizeof(InDataType));
+    std::size_t num_btype = G * NDoHoWo * CZYX * (sizeof(OutDataType) + sizeof(InDataType));
     float gb_per_sec      = num_btype / 1.E6 / ave_time;
     std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
 
     if(config.do_verification)
     {
         auto ref_image_to_column = ck::tensor_operation::host::
-            ReferenceImageToColumn<NDimSpatial, InLayout, InDataType, OutDataType>();
+            ReferenceImageToColumn<NDimSpatial, ImLayout, InDataType, OutDataType>();
 
         auto ref_invoker = ref_image_to_column.MakeInvoker();