Refine layernorm naming and test code (#497)

* Sync the naming

* Sync the test of layernorm with groupnorm

* Sync the naming

* Minor change for comment and log

* [What] Add saveMean and SaveInvVariance in the interface.
[Why] These can optimize the backward

client_example/05_layernorm/layernorm2d.cpp

@@ -90,6 +90,8 @@ int main(int argc, char* argv[])
                                                         gamma_device_buf.GetDeviceBuffer(),
                                                         beta_device_buf.GetDeviceBuffer(),
                                                         y_device_buf.GetDeviceBuffer(),
+                                                        nullptr,
+                                                        nullptr,
                                                         PassThrough{});
 
         auto invoker_ptr = op_ptr->MakeInvokerPointer();
@@ -143,6 +145,8 @@ int main(int argc, char* argv[])
                                                         gamma_device_buf.GetDeviceBuffer(),
                                                         beta_device_buf.GetDeviceBuffer(),
                                                         y_device_buf.GetDeviceBuffer(),
+                                                        nullptr,
+                                                        nullptr,
                                                         PassThrough{});
 
         auto invoker_ptr = op_ptr->MakeInvokerPointer();

example/27_layernorm/layernorm_blockwise.cpp

@@ -100,6 +100,8 @@ int main()
         gamma_dev.GetDeviceBuffer(),
         beta_dev.GetDeviceBuffer(),
         y_dev.GetDeviceBuffer(),
+        nullptr,
+        nullptr,
         PassThrough{});
 
     if(!device_instance.IsSupportedArgument(argument_ptr.get()))

example/42_groupnorm/groupnorm_sigmoid_fp16.cpp

@@ -128,6 +128,8 @@ int main(int argc, char* argv[])
         gamma_dev.GetDeviceBuffer(),
         beta_dev.GetDeviceBuffer(),
         y_dev.GetDeviceBuffer(),
+        nullptr,
+        nullptr,
         y_element_op);
 
     if(!device_instance.IsSupportedArgument(argument_ptr.get()))

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

@@ -33,6 +33,8 @@ struct DeviceNormalization : public BaseOperator
                         const void* p_gamma,
                         const void* p_beta,
                         void* p_y,
+                        void* p_savedMean,
+                        void* p_savedInvVar,
                         AccElementwiseOperation acc_elementwise_op) = 0;
 
     virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;

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

@@ -10,7 +10,7 @@
 #include "ck/tensor_operation/gpu/device/device_normalization.hpp"
 #include "ck/tensor_operation/gpu/device/device_reduce.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_layernorm_welford_variance.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_normalization_welford_variance.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_set_buffer_value.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
@@ -24,7 +24,7 @@ template <typename GridwiseReduction,
           typename AccDataType,
           typename AccElementwiseOperation,
           typename GridDesc_M_K>
-__global__ void kernel_layernorm(const GridDesc_M_K x_grid_desc_m_k,
+__global__ void kernel_normalization(const GridDesc_M_K x_grid_desc_m_k,
                                      const GridDesc_M_K gamma_grid_desc_m_k,
                                      const GridDesc_M_K beta_grid_desc_m_k,
                                      const GridDesc_M_K y_grid_desc_m_k,
@@ -54,7 +54,7 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 
-// Y = LayerNorm(X, Beta, Gamma)
+// Y = Normalization(X, Beta, Gamma)
 template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
@@ -168,7 +168,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
     using GridDesc_M_K = decltype(MakeSrc2dDescriptor({1}, {1}, 1, 1));
 
     using GridwiseReduceLayernormGeneric =
-        GridwiseLayernormWelfordVariance_mk_to_mk<XDataType,
+        GridwiseNormalizationWelfordVariance_mk_to_mk<XDataType,
                                                       GammaDataType,
                                                       BetaDataType,
                                                       YDataType,
@@ -189,8 +189,8 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                                                       XYSrcVectorDim,
                                                       YDstVectorSize,
                                                       false>;
-    using GridwiseReduceLayernormSweepOnce =
-        GridwiseLayernormWelfordVariance_mk_to_mk<XDataType,
+    using GridwiseNormalizationSweepOnce =
+        GridwiseNormalizationWelfordVariance_mk_to_mk<XDataType,
                                                       GammaDataType,
                                                       BetaDataType,
                                                       YDataType,
@@ -295,7 +295,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
         float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
         {
             const auto kernel_main = arg.isSweeponce_
-                                         ? kernel_layernorm<GridwiseReduceLayernormSweepOnce,
+                                         ? kernel_normalization<GridwiseNormalizationSweepOnce,
                                                                 XDataType,
                                                                 GammaDataType,
                                                                 BetaDataType,
@@ -303,7 +303,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                                                                 AccDataType,
                                                                 AccElementwiseOperation,
                                                                 GridDesc_M_K>
-                                         : kernel_layernorm<GridwiseReduceLayernormGeneric,
+                                         : kernel_normalization<GridwiseReduceLayernormGeneric,
                                                                 XDataType,
                                                                 GammaDataType,
                                                                 BetaDataType,
@@ -426,8 +426,16 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
                         const void* p_gamma,
                         const void* p_beta,
                         void* p_y,
+                        void* p_saveMean,
+                        void* p_saveInvVar,
                         AccElementwiseOperation acc_elementwise_op) override
     {
+        // TODO
+        // Optional cache of the intermediate results (mean and InvVariance) during the
+        // forward pass could speedup in the backward
+        ignore = p_saveMean;
+        ignore = p_saveInvVar;
+
         return std::make_unique<Argument>(lengths,
                                           xStrides,
                                           gammaStrides,

include/ck/tensor_operation/gpu/grid/gridwise_layernorm_naive_variance.hpp → include/ck/tensor_operation/gpu/grid/gridwise_normalization_naive_variance.hpp

@@ -14,7 +14,7 @@
 
 namespace ck {
 
-// Y = LayerNorm(X, Beta, Gamma)
+// Y = Normalization(X, Beta, Gamma)
 template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
@@ -36,7 +36,7 @@ template <typename XDataType,
           index_t YDstVectorDim,
           index_t YDstVectorSize,
           bool SweepOnce>
-struct GridwiseLayernormNaiveVariance_mk_to_mk
+struct GridwiseNormalizationNaiveVariance_mk_to_mk
 {
     static_assert((XSrcVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
                       (XSrcVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),

include/ck/tensor_operation/gpu/grid/gridwise_layernorm_welford_variance.hpp → include/ck/tensor_operation/gpu/grid/gridwise_normalization_welford_variance.hpp

@@ -11,7 +11,7 @@
 
 namespace ck {
 
-// Y = LayerNorm(X, Beta, Gamma)
+// Y = Normalization(X, Beta, Gamma)
 template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
@@ -33,7 +33,7 @@ template <typename XDataType,
           index_t YDstVectorDim,
           index_t YDstVectorSize,
           bool SweepOnce>
-struct GridwiseLayernormWelfordVariance_mk_to_mk
+struct GridwiseNormalizationWelfordVariance_mk_to_mk
 {
     static_assert((XSrcVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
                       (XSrcVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),

profiler/include/profile_groupnorm_impl.hpp

@@ -126,6 +126,8 @@ bool profile_groupnorm_impl(int do_verification,
             gamma_dev.GetDeviceBuffer(),
             beta_dev.GetDeviceBuffer(),
             y_dev.GetDeviceBuffer(),
+            nullptr,
+            nullptr,
             PassThrough{});
 
         if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
@@ -196,7 +198,7 @@ bool profile_groupnorm_impl(int do_verification,
 
     if(num_kernel == 0)
     {
-        std::cout << "Error: No kernel is tested" << std::endl;
+        std::cout << "Error: No kernel is applicable" << std::endl;
         return false;
     }
 

profiler/include/profile_layernorm_impl.hpp

@@ -22,7 +22,7 @@ template <typename XDataType,
           typename AccDataType,
           typename YDataType,
           index_t Rank>
-void profile_layernorm_impl(int do_verification,
+bool profile_layernorm_impl(int do_verification,
                             int init_method,
                             bool do_log,
                             bool time_kernel,
@@ -31,7 +31,7 @@ void profile_layernorm_impl(int do_verification,
     using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 
     if(length.size() < 2)
-        return;
+        return false;
 
     // Assume normalize dimension except for batch (first) dimension
     std::vector<index_t> reduce_length{length.begin() + 1, length.end()};
@@ -52,7 +52,6 @@ void profile_layernorm_impl(int do_verification,
 
     switch(init_method)
     {
-    // case 0: break;
     case 0:
         x.GenerateTensorValue(GeneratorTensor_1<XDataType>{});
         gamma.GenerateTensorValue(GeneratorTensor_1<GammaDataType>{});
@@ -122,6 +121,8 @@ void profile_layernorm_impl(int do_verification,
         ref_invoker.Run(ref_argument);
     }
 
+    int num_kernel = 0;
+
     for(auto& inst_ptr : instance_ptrs)
     {
         auto argument_ptr = inst_ptr->MakeArgumentPointer(length,
@@ -135,12 +136,21 @@ void profile_layernorm_impl(int do_verification,
                                                           gamma_dev.GetDeviceBuffer(),
                                                           beta_dev.GetDeviceBuffer(),
                                                           y_dev.GetDeviceBuffer(),
+                                                          nullptr,
+                                                          nullptr,
                                                           PassThrough{});
 
-        if(!inst_ptr->IsSupportedArgument(argument_ptr.get()))
+        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;
         }
@@ -156,6 +166,7 @@ void profile_layernorm_impl(int do_verification,
 
         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;
 
@@ -184,20 +195,32 @@ void profile_layernorm_impl(int do_verification,
             {
                 std::cout << inst_ptr->GetTypeString() << " failed verification: ";
                 LogRange(std::cout << "lengths = [", length, ", ") << "]." << std::endl;
-                return;
+                return false;
             }
             else
             {
+                if(time_kernel)
                     std::cout << "pass" << std::endl;
             }
         }
     }
 
+    if(time_kernel)
+    {
         LogRange(std::cout << "length = ", length, ",") << ", ";
         LogRange(std::cout << "stride = ", strideXY, ",") << ", ";
         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

test/normalization/CMakeLists.txt

@@ -5,8 +5,8 @@ add_gtest_executable(test_layernorm2d_fp16 test_layernorm2d_fp16.cpp)
 add_gtest_executable(test_groupnorm_fp16 test_groupnorm_fp16.cpp)
 add_gtest_executable(test_groupnorm_fp32 test_groupnorm_fp32.cpp)
 
-target_link_libraries(test_layernorm2d_fp32 PRIVATE utility)
-target_link_libraries(test_layernorm2d_fp16 PRIVATE utility)
+target_link_libraries(test_layernorm2d_fp32 PRIVATE utility device_normalization_instance)
+target_link_libraries(test_layernorm2d_fp16 PRIVATE utility device_normalization_instance)
 target_link_libraries(test_groupnorm_fp16 PRIVATE utility device_normalization_instance)
 target_link_libraries(test_groupnorm_fp32 PRIVATE utility device_normalization_instance)
 

test/normalization/test_groupnorm_fp16.cpp

@@ -20,7 +20,7 @@ class TestGroupnorm : public ::testing::Test
 
     void Run()
     {
-        // N, H, W, G, C
+        // [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},

test/normalization/test_groupnorm_fp32.cpp

@@ -20,7 +20,7 @@ class TestGroupnorm : public ::testing::Test
 
     void Run()
     {
-        // N, H, W, G, C
+        // [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},

test/normalization/test_layernorm2d_fp16.cpp

@@ -2,28 +2,44 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "test_layernorm2d_util.hpp"
+#include "profiler/include/profile_layernorm_impl.hpp"
 
-template <ck::index_t N>
-using I = ck::Number<N>;
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
 
 template <typename Tuple>
-class TestLayernorm2dFP16 : public ck::TestLayernorm2d<Tuple>
+class TestLayernorm2d : public ::testing::Test
 {
+    protected:
+    using XDataType     = std::tuple_element_t<0, Tuple>;
+    using GammaDataType = std::tuple_element_t<1, Tuple>;
+    using BetaDataType  = std::tuple_element_t<2, Tuple>;
+    using AccDataType   = std::tuple_element_t<3, Tuple>;
+    using YDataType     = std::tuple_element_t<4, Tuple>;
+
+    void Run()
+    {
+        // [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_impl<XDataType,
+                                                                GammaDataType,
+                                                                BetaDataType,
+                                                                AccDataType,
+                                                                YDataType,
+                                                                2>(true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
 };
 
-// clang-format off
 using KernelTypes = ::testing::Types<
-//  XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim , GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize>
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<8>, I<32>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>,
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<8>, I<32>, I<2>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>,
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<4>, I<64>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>,
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<4>, I<64>, I<2>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>,
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<2>, I<128>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>,
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<2>, I<128>, I<2>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>,
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>,
-    std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<1>, I<256>, I<2>, I<8>, I<1>, I<8>, I<1>, I<8>, I<1>, I<8>, I<8>>
-    >;
-// clang-format on
-TYPED_TEST_SUITE(TestLayernorm2dFP16, KernelTypes);
-TYPED_TEST(TestLayernorm2dFP16, Test_FP16) { this->Run(); }
+    // XDataType, GammaDataType, BetaDataType, AccDataType, YDataType>
+    std::tuple<F16, F16, F16, F32, F16>>;
+
+TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
+TYPED_TEST(TestLayernorm2d, Test_FP16) { this->Run(); }

test/normalization/test_layernorm2d_fp32.cpp

@@ -2,28 +2,44 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "gtest/gtest.h"
-#include "test_layernorm2d_util.hpp"
+#include "profiler/include/profile_layernorm_impl.hpp"
 
-template <ck::index_t N>
-using I = ck::Number<N>;
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
 
 template <typename Tuple>
-class TestLayernorm2dFP32 : public ck::TestLayernorm2d<Tuple>
+class TestLayernorm2d : public ::testing::Test
 {
+    protected:
+    using XDataType     = std::tuple_element_t<0, Tuple>;
+    using GammaDataType = std::tuple_element_t<1, Tuple>;
+    using BetaDataType  = std::tuple_element_t<2, Tuple>;
+    using AccDataType   = std::tuple_element_t<3, Tuple>;
+    using YDataType     = std::tuple_element_t<4, Tuple>;
+
+    void Run()
+    {
+        // [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_impl<XDataType,
+                                                                GammaDataType,
+                                                                BetaDataType,
+                                                                AccDataType,
+                                                                YDataType,
+                                                                2>(true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
 };
 
-// clang-format off
 using KernelTypes = ::testing::Types<
-//  XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize>
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<8>, I<32>, I<1>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>,
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<8>, I<32>, I<2>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>,
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<4>, I<64>, I<1>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>,
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<4>, I<64>, I<2>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>,
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<2>, I<128>, I<1>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>,
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<2>, I<128>, I<2>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>,
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>,
-    std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<1>, I<256>, I<2>, I<8>, I<1>, I<4>, I<1>, I<4>, I<1>, I<4>, I<4>>
-    >;
-// clang-format on
-TYPED_TEST_SUITE(TestLayernorm2dFP32, KernelTypes);
-TYPED_TEST(TestLayernorm2dFP32, Test_FP32) { this->Run(); }
+    // XDataType, GammaDataType, BetaDataType, AccDataType, YDataType>
+    std::tuple<F32, F32, F32, F32, F32>>;
+
+TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
+TYPED_TEST(TestLayernorm2d, Test_FP32) { this->Run(); }

test/normalization/test_layernorm2d_util.hpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
-
-#pragma once
-
-#include <vector>
-#include <iostream>
-#include <gtest/gtest.h>
-
-#include "ck/ck.hpp"
-#include "ck/utility/number.hpp"
-#include "ck/tensor_operation/gpu/device/impl/device_normalization_impl.hpp"
-
-#include "ck/library/utility/check_err.hpp"
-#include "ck/library/utility/host_tensor.hpp"
-#include "ck/library/utility/device_memory.hpp"
-#include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp"
-
-namespace ck {
-
-template <typename Range>
-std::string serialize_range(const Range& range)
-{
-    std::stringstream ss;
-    for(auto& r : range)
-    {
-        ss << r << ", ";
-    }
-    std::string str = ss.str();
-    return std::string(str.begin(), str.end() - 2);
-}
-
-template <typename Tuple>
-class TestLayernorm2d : public ::testing::Test
-{
-    protected:
-    using XDataType                             = std::tuple_element_t<0, Tuple>;
-    using GammaDataType                         = std::tuple_element_t<1, Tuple>;
-    using BetaDataType                          = std::tuple_element_t<2, Tuple>;
-    using AccDataType                           = std::tuple_element_t<3, Tuple>;
-    using YDataType                             = std::tuple_element_t<4, Tuple>;
-    static constexpr index_t Rank               = std::tuple_element_t<5, Tuple>{}.value;
-    static constexpr index_t NumReduceDim       = std::tuple_element_t<6, Tuple>{}.value;
-    static constexpr index_t BlockSize          = std::tuple_element_t<7, Tuple>{}.value;
-    static constexpr index_t MThreadClusterSize = std::tuple_element_t<8, Tuple>{}.value;
-    static constexpr index_t KThreadClusterSize = std::tuple_element_t<9, Tuple>{}.value;
-    static constexpr index_t MThreadSliceSize   = std::tuple_element_t<10, Tuple>{}.value;
-    static constexpr index_t KThreadSliceSize   = std::tuple_element_t<11, Tuple>{}.value;
-    static constexpr index_t XYSrcVectorDim     = std::tuple_element_t<12, Tuple>{}.value;
-    static constexpr index_t XSrcVectorSize     = std::tuple_element_t<13, Tuple>{}.value;
-    static constexpr index_t GammaSrcVectorDim  = std::tuple_element_t<14, Tuple>{}.value;
-    static constexpr index_t GammaSrcVectorSize = std::tuple_element_t<15, Tuple>{}.value;
-    static constexpr index_t BetaSrcVectorDim   = std::tuple_element_t<16, Tuple>{}.value;
-    static constexpr index_t BetaSrcVectorSize  = std::tuple_element_t<17, Tuple>{}.value;
-    static constexpr index_t YDstVectorSize     = std::tuple_element_t<18, Tuple>{}.value;
-
-    using PassThrough = ck::tensor_operation::element_wise::PassThrough;
-
-    using ReferenceInstance = tensor_operation::host::ReferenceLayernorm<XDataType,
-                                                                         GammaDataType,
-                                                                         BetaDataType,
-                                                                         YDataType,
-                                                                         AccDataType,
-                                                                         PassThrough,
-                                                                         Rank,
-                                                                         NumReduceDim>;
-
-    using DeviceInstance = tensor_operation::device::DeviceNormalizationImpl<XDataType,
-                                                                             GammaDataType,
-                                                                             BetaDataType,
-                                                                             AccDataType,
-                                                                             YDataType,
-                                                                             PassThrough,
-                                                                             Rank,
-                                                                             NumReduceDim,
-                                                                             BlockSize,
-                                                                             MThreadClusterSize,
-                                                                             KThreadClusterSize,
-                                                                             MThreadSliceSize,
-                                                                             KThreadSliceSize,
-                                                                             XYSrcVectorDim,
-                                                                             XSrcVectorSize,
-                                                                             GammaSrcVectorDim,
-                                                                             GammaSrcVectorSize,
-                                                                             BetaSrcVectorDim,
-                                                                             BetaSrcVectorSize,
-                                                                             YDstVectorSize>;
-
-    TestLayernorm2d() : ref_instance_invoker_(ReferenceInstance{}.MakeInvoker()) {}
-
-    void RunSingle(const std::vector<index_t>& lengths,
-                   const std::vector<index_t>& reduceDims,
-                   const std::vector<index_t>& GammaLength,
-                   const std::vector<index_t>& GammaStride,
-                   const std::vector<index_t>& BetaLength,
-                   const std::vector<index_t>& BetaStride)
-    {
-        Tensor<XDataType> x(lengths);
-        Tensor<GammaDataType> gamma(GammaLength);
-        Tensor<BetaDataType> beta(BetaLength);
-        Tensor<YDataType> y(lengths);
-        Tensor<YDataType> y_ref(lengths);
-
-        x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0, 1.0});
-        gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
-        beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});
-
-        DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
-        DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
-        DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
-        DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
-
-        x_dev.ToDevice(x.mData.data());
-        gamma_dev.ToDevice(gamma.mData.data());
-        beta_dev.ToDevice(beta.mData.data());
-
-        auto device_instance = DeviceInstance{};
-        auto argument_ptr    = device_instance.MakeArgumentPointer(
-            lengths,
-            std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
-            GammaStride,
-            BetaStride,
-            std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
-            reduceDims,
-            1e-4,
-            x_dev.GetDeviceBuffer(),
-            gamma_dev.GetDeviceBuffer(),
-            beta_dev.GetDeviceBuffer(),
-            y_dev.GetDeviceBuffer(),
-            PassThrough{});
-
-        if(!device_instance.IsSupportedArgument(argument_ptr.get()))
-        {
-            return;
-        }
-
-        auto invoker_ptr = device_instance.MakeInvokerPointer();
-        invoker_ptr->Run(argument_ptr.get());
-
-        ref_instance_invoker_.Run(
-            {x, gamma, beta, y_ref, PassThrough{}, lengths, reduceDims, 1e-4});
-
-        y_dev.FromDevice(y.mData.data());
-
-        bool pass;
-
-        if(std::is_same<XDataType, int8_t>::value)
-        {
-            EXPECT_TRUE(pass = ck::utils::check_err(
-                            y.mData, y_ref.mData, "Error: Incorrect results!", 0, 1));
-        }
-        else
-        {
-            EXPECT_TRUE(pass = ck::utils::check_err(
-                            y.mData, y_ref.mData, "Error: Incorrect results d1", 1e-3, 1e-3));
-        }
-
-        if(!pass)
-        {
-            FAIL() << "Failure in input lengths = [" << serialize_range(lengths) << "], "
-                   << "reduce dim = [" << serialize_range(reduceDims) << "].";
-        }
-    }
-
-    void Run()
-    {
-        std::vector<std::vector<index_t>> lengths = {
-            {4, 256}, {8, 511}, {9, 1032}, {4, 2048}, {1, 8192}, {4000, 2000}};
-
-        for(auto length : lengths)
-        {
-            this->RunSingle(length, {1}, {length[1]}, {0, 1}, {length[1]}, {0, 1});
-        }
-    }
-
-    typename ReferenceInstance::Invoker ref_instance_invoker_;
-};
-
-} // namespace ck