Merge branch 'develop' into amd-develop

library/src/tensor_operation_instance/gpu/permute_scale/CMakeLists.txt

+add_instance_library(device_permute_scale_instance 
+	device_permute_scale_instances.cpp)

library/src/tensor_operation_instance/gpu/permute_scale/device_permute_scale_instances.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
+#include "ck/utility/data_type.hpp"
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Pass    = ck::tensor_operation::element_wise::PassThrough;
+using UnaryOp = ck::tensor_operation::element_wise::UnarySquare;
+using Scale   = ck::tensor_operation::element_wise::Scale;
+
+// clang-format off
+using device_permute_scale_f16_instances =
+    std::tuple <
+        DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 1, ck::Sequence<1>, ck::Sequence<1>>,
+        DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 8, ck::Sequence<1>, ck::Sequence<1>>,
+        DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 4, ck::Sequence<1>, ck::Sequence<1>>,
+        DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 2, ck::Sequence<1>, ck::Sequence<1>>
+    >;
+
+using device_permute_scale_f32_instances = std::tuple<
+         DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 1, ck::Sequence<1>, ck::Sequence<1>>,
+         DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 8, ck::Sequence<1>, ck::Sequence<1>>,
+         DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 4, ck::Sequence<1>, ck::Sequence<1>>,
+         DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 2, ck::Sequence<1>, ck::Sequence<1>>
+    >;
+// clang-format on
+
+void add_device_permute_scale_f16_instances(
+    std::vector<std::unique_ptr<
+        DeviceElementwise<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4>>>& instances)
+{
+    add_device_operation_instances(instances, device_permute_scale_f16_instances{});
+}
+
+void add_device_permute_scale_f32_instances(
+    std::vector<std::unique_ptr<
+        DeviceElementwise<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4>>>& instances)
+{
+    add_device_operation_instances(instances, device_permute_scale_f32_instances{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/transpose/device_transpose_instances_3d.cpp

@@ -19,22 +19,14 @@ void add_device_transpose_f16_instances(
     std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>, ck::Tuple<F16>, PassThrough, 5>>>&
         instances)
 {
-#ifdef CK_ENABLE_FP16
     add_device_operation_instances(instances, device_transpose_f16_instances{});
-#else
-    ignore = instances;
-#endif
 }
 
 void add_device_transpose_f32_instances(
     std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>, ck::Tuple<F32>, PassThrough, 5>>>&
         instances)
 {
-#ifdef CK_ENABLE_FP32
     add_device_operation_instances(instances, device_transpose_f32_instances{});
-#else
-    ignore = instances;
-#endif
 }
 
 } // namespace instance

profiler/include/profiler/profile_groupnorm_bwd_data_impl.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iomanip>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/groupnorm_bwd_data.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"
+#include "ck/library/reference_tensor_operation/cpu/reference_groupnorm_bwd.hpp"
+
+namespace ck {
+namespace profiler {
+
+template <typename DYDataType,
+          typename XDataType,
+          typename GammaDataType,
+          typename MeanInvStdDataType,
+          typename ComputeDataType,
+          typename DXDataType>
+bool profile_groupnorm_bwd_data_impl(int do_verification,
+                                     int init_method,
+                                     bool do_log,
+                                     bool time_kernel,
+                                     std::vector<index_t> length)
+{
+    // we don't need DGamma and DBeta here, just for reference class
+    using DGammaDataType = DXDataType;
+    using DBetaDataType  = DXDataType;
+
+    if(length.size() != 5)
+        return false;
+
+    index_t N = length[0];
+    index_t G = length[3];
+    index_t C = length[4];
+
+    std::vector<index_t> reduce_dim  = {1, 2, 4};
+    std::vector<index_t> gammaLength = {G, C};
+
+    Tensor<DYDataType> dy(length);
+    Tensor<XDataType> x(length);
+    Tensor<GammaDataType> gamma({G, C});
+    Tensor<MeanInvStdDataType> mean({N, G});
+    Tensor<MeanInvStdDataType> inv_std({N, G});
+    Tensor<DXDataType> dx(length);
+
+    Tensor<DXDataType> host_dx(length);
+    Tensor<DGammaDataType> host_dgamma({G, C});
+    Tensor<DBetaDataType> host_dbeta({G, C});
+
+    std::vector<index_t> strideDy =
+        std::vector<ck::index_t>{dy.mDesc.GetStrides().begin(), dy.mDesc.GetStrides().end()};
+    std::vector<index_t> strideX  = strideDy;
+    std::vector<index_t> strideDx = strideDy;
+
+    std::vector<index_t> strideGamma      = {0, 0, 0, C, 1};
+    std::vector<index_t> strideMeanInvStd = {G, 0, 0, 1, 0};
+
+    switch(init_method)
+    {
+    case 0:
+        dy.GenerateTensorValue(GeneratorTensor_1<DYDataType>{});
+        x.GenerateTensorValue(GeneratorTensor_1<XDataType>{});
+        gamma.GenerateTensorValue(GeneratorTensor_1<GammaDataType>{});
+        mean.GenerateTensorValue(GeneratorTensor_1<MeanInvStdDataType>{});
+        inv_std.GenerateTensorValue(GeneratorTensor_1<MeanInvStdDataType>{});
+        dx.GenerateTensorValue(GeneratorTensor_1<DXDataType>{});
+        break;
+    case 1:
+        dy.GenerateTensorValue(GeneratorTensor_2<DYDataType>{-5, 5});
+        x.GenerateTensorValue(GeneratorTensor_2<XDataType>{-5, 5});
+        gamma.GenerateTensorValue(GeneratorTensor_2<GammaDataType>{-5, 5});
+        mean.GenerateTensorValue(GeneratorTensor_2<MeanInvStdDataType>{-5, 5});
+        inv_std.GenerateTensorValue(GeneratorTensor_2<MeanInvStdDataType>{-5, 5});
+        dx.GenerateTensorValue(GeneratorTensor_2<DXDataType>{-5, 5});
+        break;
+    default:
+        dy.GenerateTensorValue(GeneratorTensor_3<DYDataType>{0, 1});
+        x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0, 1});
+        gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-0.5, 0.5});
+        mean.GenerateTensorValue(GeneratorTensor_3<MeanInvStdDataType>{-0.5, 0.5});
+        inv_std.GenerateTensorValue(GeneratorTensor_3<MeanInvStdDataType>{-0.5, 0.5});
+        dx.GenerateTensorValue(GeneratorTensor_3<DXDataType>{-0.5, 0.5});
+    }
+
+    DeviceMem dy_dev(sizeof(DYDataType) * dy.mDesc.GetElementSpaceSize());
+    DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
+    DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
+    DeviceMem mean_dev(sizeof(MeanInvStdDataType) * mean.mDesc.GetElementSpaceSize());
+    DeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * inv_std.mDesc.GetElementSpaceSize());
+    DeviceMem dx_dev(sizeof(DXDataType) * dx.mDesc.GetElementSpaceSize());
+
+    dy_dev.ToDevice(dy.mData.data());
+    x_dev.ToDevice(x.mData.data());
+    gamma_dev.ToDevice(gamma.mData.data());
+    mean_dev.ToDevice(mean.mData.data());
+    inv_std_dev.ToDevice(inv_std.mData.data());
+
+    // add device normalization instances
+    using DeviceOp = ck::tensor_operation::device::DeviceNormalizationBwdData<DYDataType,
+                                                                              XDataType,
+                                                                              GammaDataType,
+                                                                              MeanInvStdDataType,
+                                                                              DXDataType,
+                                                                              5,
+                                                                              3>;
+
+    // get device op instances
+    const auto instance_ptrs =
+        ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+            DeviceOp>::GetInstances();
+
+    std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
+
+    std::string best_instance_name;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+
+    if(do_verification)
+    {
+        using ReferenceInstance =
+            ck::tensor_operation::host::ReferenceGroupnormBwd<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, length);
+        auto ref_invoker = ref.MakeInvoker();
+        ref_invoker.Run(ref_argument);
+    }
+
+    int num_kernel = 0;
+
+    for(auto& inst_ptr : instance_ptrs)
+    {
+        auto argument_ptr = inst_ptr->MakeArgumentPointer(length,
+                                                          strideDy,
+                                                          strideX,
+                                                          strideGamma,
+                                                          strideMeanInvStd,
+                                                          strideMeanInvStd,
+                                                          strideDx,
+                                                          reduce_dim,
+                                                          dy_dev.GetDeviceBuffer(),
+                                                          x_dev.GetDeviceBuffer(),
+                                                          gamma_dev.GetDeviceBuffer(),
+                                                          mean_dev.GetDeviceBuffer(),
+                                                          inv_std_dev.GetDeviceBuffer(),
+                                                          dx_dev.GetDeviceBuffer());
+
+        if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            ++num_kernel;
+        }
+        else
+        {
+            if(time_kernel)
+            {
+                std::cout << inst_ptr->GetTypeString() << " skipped due to unsupported argument: ";
+                LogRange(std::cout << "input lengths = ", length, ", ") << std::endl;
+            }
+
+            continue;
+        }
+
+        size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
+        DeviceMem workspace_dev(workspace_sz);
+        inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
+
+        auto invoker_ptr = inst_ptr->MakeInvokerPointer();
+
+        float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
+
+        std::size_t num_bytes = dy.mDesc.GetElementSize() * sizeof(DYDataType) +
+                                x.mDesc.GetElementSize() * sizeof(XDataType) +
+                                gamma.mDesc.GetElementSize() * sizeof(GammaDataType) +
+                                mean.mDesc.GetElementSize() * sizeof(MeanInvStdDataType) +
+                                inv_std.mDesc.GetElementSize() * sizeof(MeanInvStdDataType) +
+                                dx.mDesc.GetElementSize() * sizeof(DXDataType);
+
+        float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+        if(time_kernel)
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
+                      << inst_ptr->GetTypeString() << std::endl;
+
+        if(avg_time < best_avg_time)
+        {
+            best_instance_name = inst_ptr->GetTypeString();
+            best_avg_time      = avg_time;
+            best_gb_per_sec    = gb_per_sec;
+        }
+
+        if(do_verification)
+        {
+            dx_dev.FromDevice(dx.mData.data());
+            bool pass = ck::utils::check_err(
+                dx.mData, host_dx.mData, "Error: Incorrect results", 1e-3, 1e-3);
+
+            if(do_log)
+            {
+                LogRangeAsType<float>(std::cout << "dy  : ", dy.mData, ",") << std::endl;
+                LogRangeAsType<float>(std::cout << "host_dx  : ", host_dx.mData, ",") << std::endl;
+                LogRangeAsType<float>(std::cout << "dx  : ", dx.mData, ",") << std::endl;
+            }
+
+            if(!pass)
+            {
+                std::cout << inst_ptr->GetTypeString() << " failed verification: ";
+                LogRange(std::cout << "lengths = [", length, ", ") << "]." << std::endl;
+                return false;
+            }
+            else
+            {
+                if(time_kernel)
+                    std::cout << "pass" << std::endl;
+            }
+        }
+    }
+
+    if(time_kernel)
+    {
+        LogRange(std::cout << "length = ", length, ",") << ", ";
+        LogRange(std::cout << "reduce dims ", reduce_dim, ",") << std::endl;
+        std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s,"
+                  << best_instance_name << std::endl;
+    }
+
+    if(num_kernel == 0)
+    {
+        std::cout << "Error: No kernel is applicable" << std::endl;
+        return false;
+    }
+
+    return true;
+}
+
+} // namespace profiler
+} // namespace ck

profiler/include/profiler/profile_layernorm_bwd_data_impl.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iomanip>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/layernorm_bwd_data.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"
+#include "ck/library/reference_tensor_operation/cpu/reference_layernorm_bwd.hpp"
+
+namespace ck {
+namespace profiler {
+
+template <typename DYDataType,
+          typename XDataType,
+          typename GammaDataType,
+          typename MeanInvStdDataType,
+          typename ComputeDataType,
+          typename DXDataType,
+          index_t Rank>
+bool profile_layernorm_bwd_data_impl(int do_verification,
+                                     int init_method,
+                                     bool do_log,
+                                     bool time_kernel,
+                                     std::vector<index_t> length)
+{
+    // we don't need DGamma and DBeta here, just for reference class
+    using DGammaDataType = DXDataType;
+    using DBetaDataType  = DXDataType;
+
+    if(length.size() != Rank || Rank < 2)
+        return false;
+
+    // Assume normalize dimension except for batch (first) dimension
+    std::vector<index_t> reduce_length{length.begin() + 1, length.end()};
+    std::vector<index_t> reduce_dim;
+    for(int i = 1; i < Rank; ++i)
+        reduce_dim.push_back(i);
+
+    Tensor<DYDataType> dy(length);
+    Tensor<XDataType> x(length);
+    Tensor<GammaDataType> gamma(reduce_length);
+    Tensor<MeanInvStdDataType> mean({length[0]});
+    Tensor<MeanInvStdDataType> inv_std({length[0]});
+    Tensor<DXDataType> dx(length);
+
+    Tensor<DXDataType> host_dx(length);
+    Tensor<DGammaDataType> host_dgamma(reduce_length);
+    Tensor<DBetaDataType> host_dbeta(reduce_length);
+
+    std::vector<index_t> strideDy =
+        std::vector<ck::index_t>{dy.mDesc.GetStrides().begin(), dy.mDesc.GetStrides().end()};
+    std::vector<index_t> strideX  = strideDy;
+    std::vector<index_t> strideDx = strideDy;
+
+    std::vector<index_t> strideGamma = strideDy;
+    strideGamma[0]                   = 0;
+
+    std::vector<index_t> strideMeanInvStd{Rank, 0};
+    strideMeanInvStd[0] = 1;
+
+    switch(init_method)
+    {
+    case 0:
+        dy.GenerateTensorValue(GeneratorTensor_1<DYDataType>{});
+        x.GenerateTensorValue(GeneratorTensor_1<XDataType>{});
+        gamma.GenerateTensorValue(GeneratorTensor_1<GammaDataType>{});
+        mean.GenerateTensorValue(GeneratorTensor_1<MeanInvStdDataType>{});
+        inv_std.GenerateTensorValue(GeneratorTensor_1<MeanInvStdDataType>{});
+        dx.GenerateTensorValue(GeneratorTensor_1<DXDataType>{});
+        break;
+    case 1:
+        dy.GenerateTensorValue(GeneratorTensor_2<DYDataType>{-5, 5});
+        x.GenerateTensorValue(GeneratorTensor_2<XDataType>{-5, 5});
+        gamma.GenerateTensorValue(GeneratorTensor_2<GammaDataType>{-5, 5});
+        mean.GenerateTensorValue(GeneratorTensor_2<MeanInvStdDataType>{-5, 5});
+        inv_std.GenerateTensorValue(GeneratorTensor_2<MeanInvStdDataType>{-5, 5});
+        dx.GenerateTensorValue(GeneratorTensor_2<DXDataType>{-5, 5});
+        break;
+    default:
+        dy.GenerateTensorValue(GeneratorTensor_3<DYDataType>{0, 1});
+        x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0, 1});
+        gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-0.5, 0.5});
+        mean.GenerateTensorValue(GeneratorTensor_3<MeanInvStdDataType>{-0.5, 0.5});
+        inv_std.GenerateTensorValue(GeneratorTensor_3<MeanInvStdDataType>{-0.5, 0.5});
+        dx.GenerateTensorValue(GeneratorTensor_3<DXDataType>{-0.5, 0.5});
+    }
+
+    DeviceMem dy_dev(sizeof(DYDataType) * dy.mDesc.GetElementSpaceSize());
+    DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
+    DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
+    DeviceMem mean_dev(sizeof(MeanInvStdDataType) * mean.mDesc.GetElementSpaceSize());
+    DeviceMem inv_std_dev(sizeof(MeanInvStdDataType) * inv_std.mDesc.GetElementSpaceSize());
+    DeviceMem dx_dev(sizeof(DXDataType) * dx.mDesc.GetElementSpaceSize());
+
+    dy_dev.ToDevice(dy.mData.data());
+    x_dev.ToDevice(x.mData.data());
+    gamma_dev.ToDevice(gamma.mData.data());
+    mean_dev.ToDevice(mean.mData.data());
+    inv_std_dev.ToDevice(inv_std.mData.data());
+
+    constexpr int NumReduceDim = Rank - 1;
+
+    // add device normalization instances
+    using DeviceOp = ck::tensor_operation::device::DeviceNormalizationBwdData<DYDataType,
+                                                                              XDataType,
+                                                                              GammaDataType,
+                                                                              MeanInvStdDataType,
+                                                                              DXDataType,
+                                                                              Rank,
+                                                                              NumReduceDim>;
+
+    // get device op instances
+    const auto instance_ptrs =
+        ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+            DeviceOp>::GetInstances();
+
+    std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
+
+    std::string best_instance_name;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+
+    if(do_verification)
+    {
+        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, length);
+        auto ref_invoker = ref.MakeInvoker();
+        ref_invoker.Run(ref_argument);
+    }
+
+    int num_kernel = 0;
+
+    for(auto& inst_ptr : instance_ptrs)
+    {
+        auto argument_ptr = inst_ptr->MakeArgumentPointer(length,
+                                                          strideDy,
+                                                          strideX,
+                                                          strideGamma,
+                                                          strideMeanInvStd,
+                                                          strideMeanInvStd,
+                                                          strideDx,
+                                                          reduce_dim,
+                                                          dy_dev.GetDeviceBuffer(),
+                                                          x_dev.GetDeviceBuffer(),
+                                                          gamma_dev.GetDeviceBuffer(),
+                                                          mean_dev.GetDeviceBuffer(),
+                                                          inv_std_dev.GetDeviceBuffer(),
+                                                          dx_dev.GetDeviceBuffer());
+
+        if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            ++num_kernel;
+        }
+        else
+        {
+            if(time_kernel)
+            {
+                std::cout << inst_ptr->GetTypeString() << " skipped due to unsupported argument: ";
+                LogRange(std::cout << "input lengths = ", length, ", ") << std::endl;
+            }
+
+            continue;
+        }
+
+        size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
+        DeviceMem workspace_dev(workspace_sz);
+        inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
+
+        auto invoker_ptr = inst_ptr->MakeInvokerPointer();
+
+        float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
+
+        std::size_t num_bytes = dy.mDesc.GetElementSize() * sizeof(DYDataType) +
+                                x.mDesc.GetElementSize() * sizeof(XDataType) +
+                                gamma.mDesc.GetElementSize() * sizeof(GammaDataType) +
+                                mean.mDesc.GetElementSize() * sizeof(MeanInvStdDataType) +
+                                inv_std.mDesc.GetElementSize() * sizeof(MeanInvStdDataType) +
+                                dx.mDesc.GetElementSize() * sizeof(DXDataType);
+
+        float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+        if(time_kernel)
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
+                      << inst_ptr->GetTypeString() << std::endl;
+
+        if(avg_time < best_avg_time)
+        {
+            best_instance_name = inst_ptr->GetTypeString();
+            best_avg_time      = avg_time;
+            best_gb_per_sec    = gb_per_sec;
+        }
+
+        if(do_verification)
+        {
+            dx_dev.FromDevice(dx.mData.data());
+            bool pass = ck::utils::check_err(
+                dx.mData, host_dx.mData, "Error: Incorrect results", 1e-3, 1e-3);
+
+            if(do_log)
+            {
+                LogRangeAsType<float>(std::cout << "dy  : ", dy.mData, ",") << std::endl;
+                LogRangeAsType<float>(std::cout << "host_dx  : ", host_dx.mData, ",") << std::endl;
+                LogRangeAsType<float>(std::cout << "dx  : ", dx.mData, ",") << std::endl;
+            }
+
+            if(!pass)
+            {
+                std::cout << inst_ptr->GetTypeString() << " failed verification: ";
+                LogRange(std::cout << "lengths = [", length, ", ") << "]." << std::endl;
+                return false;
+            }
+            else
+            {
+                if(time_kernel)
+                    std::cout << "pass" << std::endl;
+            }
+        }
+    }
+
+    if(time_kernel)
+    {
+        LogRange(std::cout << "length = ", length, ",") << ", ";
+        LogRange(std::cout << "reduce dims ", reduce_dim, ",") << std::endl;
+        std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s,"
+                  << best_instance_name << std::endl;
+    }
+
+    if(num_kernel == 0)
+    {
+        std::cout << "Error: No kernel is applicable" << std::endl;
+        return false;
+    }
+
+    return true;
+}
+
+} // namespace profiler
+} // namespace ck

profiler/src/CMakeLists.txt

@@ -16,7 +16,9 @@ set(PROFILER_SOURCES
     profile_grouped_conv_fwd.cpp
     profile_grouped_conv_bwd_weight.cpp
     profile_reduce.cpp
+    profile_groupnorm_bwd_data.cpp
     profile_groupnorm_fwd.cpp
+    profile_layernorm_bwd_data.cpp
     profile_layernorm_fwd.cpp
     profile_max_pool3d_fwd.cpp
     profile_avg_pool3d_bwd.cpp
@@ -78,6 +80,7 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_w
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_add_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_fwd_instance)
+target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_bwd_data_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
 target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)

profiler/src/profile_groupnorm_bwd_data.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <vector>
+#include <unordered_map>
+
+#include "profiler/data_type_enum.hpp"
+#include "profiler/profile_groupnorm_bwd_data_impl.hpp"
+#include "profiler_operation_registry.hpp"
+
+using ck::index_t;
+
+struct groupnormBwdDataArgParser
+{
+    std::unordered_map<std::string, std::vector<int>> long_opts = {{"length", {}}};
+
+    bool parse_opt(int argc, char* argv[], const std::string& key, int i)
+    {
+        if(std::string("--") + key == argv[i])
+        {
+            int pos = i;
+            while(++i < argc && argv[i][0] != '-') {}
+            int end = i;
+            for(int j = pos + 1; j < end; j++)
+            {
+                long_opts[key].push_back(std::stoi(argv[j]));
+            }
+            return true;
+        }
+        return false;
+    }
+
+    void operator()(int argc, char* argv[])
+    {
+        for(auto& kv : long_opts)
+        {
+            for(int i = 1; i < argc; i++)
+            {
+                if(parse_opt(argc, argv, kv.first, i))
+                    break;
+            }
+        }
+    }
+};
+
+void print_help_groupnorm_bwd_data()
+{
+    // eg: ckProfiler groupnorm_bwd_data 1 0 2 0 1 --length 1 16 16 32 40
+    std::cout << "arg1: data type (0: fp16; 1: fp32)\n"
+              << "arg2: verification (0: no; 1: yes)\n"
+              << "arg3: initialization (0: no init; 1: integer value; 2: decimal value)\n"
+              << "arg4: print tensor value (0: no; 1: yes)\n"
+              << "arg5: time kernel (0=no, 1=yes)\n"
+              << "--length: tensor extents (e.g, --length 1 16 16 32 40) \n"
+              << std::endl;
+}
+
+int profile_groupnorm_bwd_data(int argc, char* argv[])
+{
+    if(argc <= 2)
+    {
+        print_help_groupnorm_bwd_data();
+        return 0;
+    }
+
+    groupnormBwdDataArgParser arg_parser;
+
+    // short unnamed options
+    const ck::DataTypeEnum data_type = static_cast<ck::DataTypeEnum>(std::stoi(argv[2]));
+    const bool do_verification       = std::stoi(argv[3]);
+    const int init_method            = std::stoi(argv[4]);
+    const bool do_log                = std::stoi(argv[5]);
+    const bool time_kernel           = std::stoi(argv[6]);
+
+    // parse the long options
+    arg_parser(argc, argv);
+    const std::vector<index_t> length = arg_parser.long_opts["length"];
+
+    using F32 = float;
+
+    if(length.size() == 5)
+    {
+        if(data_type == ck::DataTypeEnum::Float)
+        {
+            ck::profiler::profile_groupnorm_bwd_data_impl<F32, F32, F32, F32, F32, F32>(
+                do_verification, init_method, do_log, time_kernel, length);
+        }
+        else
+        {
+            throw std::runtime_error("not implemented yet");
+        }
+    }
+    else
+    {
+        throw std::runtime_error("length should be 5");
+    }
+
+    return 0;
+}
+
+REGISTER_PROFILER_OPERATION("groupnorm_bwd_data",
+                            "Group Normalization",
+                            profile_groupnorm_bwd_data);

profiler/src/profile_groupnorm_fwd.cpp

@@ -98,7 +98,7 @@ int profile_groupnorm(int argc, char* argv[])
     }
     else if(data_type == ck::DataTypeEnum::Half)
     {
-        ck::profiler::profile_groupnorm_impl<F16, F16, F16, F32, F16, F32, false>(
+        ck::profiler::profile_groupnorm_impl<F16, F16, F16, F32, F16, F16, false>(
             do_verification, init_method, do_log, time_kernel, length);
     }
     else

profiler/src/profile_layernorm_bwd_data.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <vector>
+#include <unordered_map>
+
+#include "profiler/data_type_enum.hpp"
+#include "profiler/profile_layernorm_bwd_data_impl.hpp"
+#include "profiler_operation_registry.hpp"
+
+using ck::index_t;
+
+struct layernormBwdDataArgParser
+{
+    std::unordered_map<std::string, std::vector<int>> long_opts = {{"length", {}}};
+
+    bool parse_opt(int argc, char* argv[], const std::string& key, int i)
+    {
+        if(std::string("--") + key == argv[i])
+        {
+            int pos = i;
+            while(++i < argc && argv[i][0] != '-') {}
+            int end = i;
+            for(int j = pos + 1; j < end; j++)
+            {
+                long_opts[key].push_back(std::stoi(argv[j]));
+            }
+            return true;
+        }
+        return false;
+    }
+
+    void operator()(int argc, char* argv[])
+    {
+        for(auto& kv : long_opts)
+        {
+            for(int i = 1; i < argc; i++)
+            {
+                if(parse_opt(argc, argv, kv.first, i))
+                    break;
+            }
+        }
+    }
+};
+
+void print_help_layernorm_bwd_data()
+{
+    // eg: ckProfiler layernorm_bwd_data 0 0 2 0 1 --length 1502 4096
+    std::cout << "arg1: data type (0: fp16; 1: fp32)\n"
+              << "arg2: verification (0: no; 1: yes)\n"
+              << "arg3: initialization (0: no init; 1: integer value; 2: decimal value)\n"
+              << "arg4: print tensor value (0: no; 1: yes)\n"
+              << "arg5: time kernel (0=no, 1=yes)\n"
+              << "--length: tensor extents (e.g, --length 1024 1024) \n"
+              << std::endl;
+}
+
+int profile_layernorm_bwd_data(int argc, char* argv[])
+{
+    if(argc <= 2)
+    {
+        print_help_layernorm_bwd_data();
+        return 0;
+    }
+
+    layernormBwdDataArgParser arg_parser;
+
+    // short unnamed options
+    const ck::DataTypeEnum data_type = static_cast<ck::DataTypeEnum>(std::stoi(argv[2]));
+    const bool do_verification       = std::stoi(argv[3]);
+    const int init_method            = std::stoi(argv[4]);
+    const bool do_log                = std::stoi(argv[5]);
+    const bool time_kernel           = std::stoi(argv[6]);
+
+    // parse the long options
+    arg_parser(argc, argv);
+    const std::vector<index_t> length = arg_parser.long_opts["length"];
+
+    using F16 = ck::half_t;
+    using F32 = float;
+
+    if(length.size() == 2)
+    {
+        constexpr int rank = 2;
+
+        if(data_type == ck::DataTypeEnum::Half)
+        {
+            ck::profiler::profile_layernorm_bwd_data_impl<F16, F16, F16, F16, F32, F16, rank>(
+                do_verification, init_method, do_log, time_kernel, length);
+        }
+        else if(data_type == ck::DataTypeEnum::Float)
+        {
+            ck::profiler::profile_layernorm_bwd_data_impl<F32, F32, F32, F32, F32, F32, rank>(
+                do_verification, init_method, do_log, time_kernel, length);
+        }
+        else
+        {
+            throw std::runtime_error("not implemented yet");
+        }
+    }
+    else
+    {
+        throw std::runtime_error("not implemented yet");
+    }
+
+    return 0;
+}
+
+REGISTER_PROFILER_OPERATION("layernorm_bwd_data",
+                            "Layer Normalization",
+                            profile_layernorm_bwd_data);

profiler/src/profile_layernorm_fwd.cpp

@@ -104,7 +104,7 @@ int profile_layernorm(int argc, char* argv[])
 
         if(data_type == ck::DataTypeEnum::Half)
         {
-            ck::profiler::profile_layernorm_impl<F16, F16, F16, F32, F16, F32, false, rank>(
+            ck::profiler::profile_layernorm_impl<F16, F16, F16, F32, F16, F16, false, rank>(
                 do_verification, init_method, do_log, time_kernel, length);
         }
         else if(data_type == ck::DataTypeEnum::Float)
@@ -125,4 +125,4 @@ int profile_layernorm(int argc, char* argv[])
     return 0;
 }
 
-REGISTER_PROFILER_OPERATION("layernorm", "Layer Normalization", profile_layernorm);
+REGISTER_PROFILER_OPERATION("layernorm_fwd", "Layer Normalization", profile_layernorm);

test/CMakeLists.txt

@@ -140,6 +140,7 @@ add_subdirectory(grouped_convnd_bwd_weight)
 add_subdirectory(block_to_ctile_map)
 add_subdirectory(softmax)
 add_subdirectory(normalization_fwd)
+add_subdirectory(normalization_bwd_data)
 add_subdirectory(data_type)
 add_subdirectory(elementwise_normalization)
 add_subdirectory(batchnorm)
@@ -149,6 +150,7 @@ add_subdirectory(batched_gemm_multi_d)
 add_subdirectory(grouped_convnd_bwd_data)
 add_subdirectory(conv_tensor_rearrange)
 add_subdirectory(transpose)
+add_subdirectory(permute_scale)
 add_subdirectory(wrapper)
 if(GPU_TARGETS MATCHES "gfx11")
     add_subdirectory(wmma_op)

test/normalization_bwd_data/CMakeLists.txt

+add_custom_target(test_normalization_bwd_data)
+add_gtest_executable(test_layernorm2d_bwd_data_fp32 test_layernorm2d_bwd_data_fp32.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_layernorm2d_bwd_data_fp32 PRIVATE utility device_normalization_bwd_data_instance)
+  add_dependencies(test_normalization_bwd_data test_layernorm2d_bwd_data_fp32)
+endif()
+
+add_gtest_executable(test_groupnorm_bwd_data_fp32 test_groupnorm_bwd_data_fp32.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_groupnorm_bwd_data_fp32 PRIVATE utility device_normalization_bwd_data_instance)
+  add_dependencies(test_normalization_bwd_data test_groupnorm_bwd_data_fp32)
+endif()
+

test/normalization_bwd_data/test_groupnorm_bwd_data_fp32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "gtest/gtest.h"
+#include "profiler/profile_groupnorm_bwd_data_impl.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
+
+template <typename Tuple>
+class TestgroupnormBwdData : public ::testing::Test
+{
+    protected:
+    using DYDataType         = std::tuple_element_t<0, Tuple>;
+    using XDataType          = std::tuple_element_t<1, Tuple>;
+    using GammaDataType      = std::tuple_element_t<2, Tuple>;
+    using MeanInvStdDataType = std::tuple_element_t<3, Tuple>;
+    using ComputeDataType    = std::tuple_element_t<4, Tuple>;
+    using DXDataType         = std::tuple_element_t<5, Tuple>;
+
+    void Run()
+    {
+        // Bwd data: [N, H, W, G, C], reduce H, W, C
+        std::vector<std::vector<ck::index_t>> lengths = {{1, 1, 1, 1, 1},
+                                                         {1, 2, 3, 4, 5},
+                                                         {256, 9, 9, 9, 9},
+                                                         {1, 64, 64, 32, 10},
+                                                         {1, 32, 32, 32, 20},
+                                                         {1, 16, 16, 32, 40}};
+
+        for(auto length : lengths)
+        {
+            bool success = ck::profiler::profile_groupnorm_bwd_data_impl<DYDataType,
+                                                                         XDataType,
+                                                                         GammaDataType,
+                                                                         MeanInvStdDataType,
+                                                                         ComputeDataType,
+                                                                         DXDataType>(
+                true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
+};
+
+using KernelTypes = ::testing::Types<
+    // DYDataType XDataType, GammaDataType, MeanInvStdDataType, ComputeDataType, DXDataType>
+    std::tuple<F32, F32, F32, F32, F32, F32>>;
+
+TYPED_TEST_SUITE(TestgroupnormBwdData, KernelTypes);
+TYPED_TEST(TestgroupnormBwdData, Test_FP32) { this->Run(); }

test/normalization_bwd_data/test_layernorm2d_bwd_data_fp32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "gtest/gtest.h"
+#include "profiler/profile_layernorm_bwd_data_impl.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
+
+template <typename Tuple>
+class TestLayernorm2dBwdData : public ::testing::Test
+{
+    protected:
+    using DYDataType         = std::tuple_element_t<0, Tuple>;
+    using XDataType          = std::tuple_element_t<1, Tuple>;
+    using GammaDataType      = std::tuple_element_t<2, Tuple>;
+    using MeanInvStdDataType = std::tuple_element_t<3, Tuple>;
+    using ComputeDataType    = std::tuple_element_t<4, Tuple>;
+    using DXDataType         = std::tuple_element_t<5, Tuple>;
+
+    void Run()
+    {
+        // Bwd data: [N, D], reduce D
+        std::vector<std::vector<ck::index_t>> lengths = {
+            {4, 256}, {8, 511}, {9, 1032}, {4, 2048}, {1, 8192}, {4000, 2000}};
+
+        for(auto length : lengths)
+        {
+            bool success =
+                ck::profiler::profile_layernorm_bwd_data_impl<DYDataType,
+                                                              XDataType,
+                                                              GammaDataType,
+                                                              MeanInvStdDataType,
+                                                              ComputeDataType,
+                                                              DXDataType,
+                                                              2>(true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
+};
+
+using KernelTypes = ::testing::Types<
+    // DYDataType XDataType, GammaDataType, MeanInvStdDataType, ComputeDataType, DXDataType>
+    std::tuple<F32, F32, F32, F32, F32, F32>>;
+
+TYPED_TEST_SUITE(TestLayernorm2dBwdData, KernelTypes);
+TYPED_TEST(TestLayernorm2dBwdData, Test_FP32) { this->Run(); }

test/normalization_fwd/test_groupnorm_fwd_fp16.cpp

@@ -47,8 +47,8 @@ class TestGroupnorm : public ::testing::Test
 };
 
 using KernelTypes = ::testing::Types<
-    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
-    std::tuple<F16, F16, F16, F32, F16, F32>>;
+    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
+    std::tuple<F16, F16, F16, F32, F16, F16>>;
 
 TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);
 TYPED_TEST(TestGroupnorm, Test_FP16) { this->Run(); }

test/normalization_fwd/test_groupnorm_fwd_fp32.cpp

@@ -45,7 +45,7 @@ class TestGroupnorm : public ::testing::Test
 };
 
 using KernelTypes = ::testing::Types<
-    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
+    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
     std::tuple<F32, F32, F32, F32, F32, F32>>;
 
 TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);

test/normalization_fwd/test_layernorm2d_fwd_fp16.cpp

@@ -41,8 +41,8 @@ class TestLayernorm2d : public ::testing::Test
 };
 
 using KernelTypes = ::testing::Types<
-    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
-    std::tuple<F16, F16, F16, F32, F16, F32>>;
+    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
+    std::tuple<F16, F16, F16, F32, F16, F16>>;
 
 TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
 TYPED_TEST(TestLayernorm2d, Test_FP16) { this->Run(); }

test/normalization_fwd/test_layernorm4d_fwd_fp16.cpp

@@ -41,8 +41,8 @@ class TestLayernorm4d : public ::testing::Test
 };
 
 using KernelTypes = ::testing::Types<
-    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
-    std::tuple<F16, F16, F16, F32, F16, F32>>;
+    // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
+    std::tuple<F16, F16, F16, F32, F16, F16>>;
 
 TYPED_TEST_SUITE(TestLayernorm4d, KernelTypes);
 TYPED_TEST(TestLayernorm4d, Test_FP16) { this->Run(); }

test/permute_scale/CMakeLists.txt

+add_custom_target(test_permute)
+add_gtest_executable(test_permute_scale test_permute_scale.cpp)
+if(result EQUAL 0)
+  target_link_libraries(test_permute_scale PRIVATE utility device_permute_scale_instance)
+  add_dependencies(test_permute test_permute_scale)
+endif()

test/permute_scale/test_permute_scale.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "gtest/gtest.h"
+#include "test_permute_scale_impl.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
+
+template <typename Tuple>
+class TestPermute : public ::testing::Test
+{
+    protected:
+    using ADataType = std::tuple_element_t<0, Tuple>;
+    using BDataType = std::tuple_element_t<1, Tuple>;
+
+    void Run()
+    {
+        std::vector<std::vector<ck::index_t>> lengths = {
+            {4, 2, 1, 8}, {1, 1, 1, 1}, {16, 8, 32, 64}, {32, 64, 128, 128}};
+
+        for(auto length : lengths)
+        {
+            bool success =
+                ck::test_permute_scale_impl<ADataType, BDataType, 4>(true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
+};
+
+using KernelTypes = ::testing::Types<std::tuple<F16, F16>, std::tuple<F32, F32>>;
+
+TYPED_TEST_SUITE(TestPermute, KernelTypes);
+TYPED_TEST(TestPermute, Test_FP16) { this->Run(); }
+TYPED_TEST(TestPermute, Test_FP32) { this->Run(); }