Merge branch 'develop' into amd-develop

cmake/ClangTidy.cmake

@@ -149,7 +149,7 @@ function(clang_tidy_check TARGET)
             add_custom_target(${tidy_target}
                 # for some targets clang-tidy not able to get information from .clang-tidy
                 DEPENDS ${SOURCE}
-                COMMAND ${CLANG_TIDY_COMMAND} "-config=\{CheckOptions: \[\{key: bugprone-reserved-identifier.AllowedIdentifiers,value: __HIP_PLATFORM_HCC__\; __HIP_ROCclr__\}\]\}" ${SOURCE} "-export-fixes=${CLANG_TIDY_FIXIT_DIR}/${TARGET}-${tidy_file}.yaml"
+                COMMAND ${CLANG_TIDY_COMMAND} "-config=\{CheckOptions: \[\{key: bugprone-reserved-identifier.AllowedIdentifiers,value: __HIP_PLATFORM_HCC__\; __HIP_PLATFORM_AMD__\; __HIP_ROCclr__\}\]\}" ${SOURCE} "-export-fixes=${CLANG_TIDY_FIXIT_DIR}/${TARGET}-${tidy_file}.yaml"
                 WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
                 COMMENT "clang-tidy: Running clang-tidy on target ${SOURCE}..."
             )

docs/sphinx/requirements.in

-rocm-docs-core==0.30.1
+rocm-docs-core==0.30.2
 sphinxcontrib-bibtex==2.6.1

docs/sphinx/requirements.txt

@@ -113,7 +113,7 @@ requests==2.31.0
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core==0.30.1
+rocm-docs-core==0.30.2
     # via -r requirements.in
 six==1.16.0
     # via

example/44_elementwise_permute/elementwise_permute_4D_fp16_col.cpp

 #include <iostream>
 #include <cstdlib>
+#include <random>
 
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
@@ -48,10 +49,8 @@ void host_elementwise4D(HostTensorB& B_nhwc,
                 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);
                 }
@@ -62,12 +61,14 @@ 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};
+    std::vector<std::size_t> nchw = {16, 8, 32, 64};
+    std::vector<std::size_t> nhwc = {16, 32, 64, 8};
     Tensor<ADataType> a(nchw);
     Tensor<BDataType> b(nhwc);
     float scale = 1.f;
     auto i      = 0;
+    std::mt19937 gen(11939);
+    std::uniform_int_distribution<int> dis(0, 1);
     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)
@@ -75,7 +76,7 @@ int main()
                 {
                     a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
                             (h * nchw[3]) + w] = i;
-                    i++;
+                    i                          = dis(gen);
                 }
 
     DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());

example/44_elementwise_permute/elementwise_permute_4D_fp32_col.cpp

@@ -67,6 +67,8 @@ int main()
 
     float scale = 1.f;
     auto i      = 0;
+    std::mt19937 gen(11939);
+    std::uniform_int_distribution<int> dis(0, 1);
     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)
@@ -74,7 +76,7 @@ int main()
                 {
                     a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
                             (h * nchw[3]) + w] = i;
-                    i++;
+                    i                          = dis(gen);
                 }
 
     DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());

example/53_layernorm2d_bwd/CMakeLists.txt

+add_example_executable(example_layernorm2d_bwd_fp32 layernorm2d_bwd_fp32.cpp)

example/53_layernorm_bwd/layernorm2d_bwd_fp16.cpp → example/53_layernorm2d_bwd/layernorm2d_bwd_fp32.cpp

@@ -15,16 +15,17 @@
 #include "ck/library/utility/literals.hpp"
 
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_normalization_bwd_data_impl.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_normalization_bwd_gamma_beta_impl.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_layernorm_bwd.hpp"
 
-using DYDataType         = ck::half_t;
-using XDataType          = ck::half_t;
-using GammaDataType      = ck::half_t;
+using DYDataType         = float;
+using XDataType          = float;
+using GammaDataType      = float;
 using MeanInvStdDataType = float;
-using DGammaDataType     = ck::half_t;
-using DBetaDataType      = ck::half_t;
-using DXDataType         = ck::half_t;
+using DGammaDataType     = float;
+using DBetaDataType      = float;
+using DXDataType         = float;
 using ComputeDataType    = float;
 
 constexpr int Rank         = 2;
@@ -39,6 +40,7 @@ constexpr int NumReduceDim = 1;
 // inv_std:   [M, 1]
 
 // Output shape
+// dx:     [M, N]
 // dgamma: [1, N]
 // dbeta:  [1, N]
 
@@ -46,8 +48,34 @@ constexpr int NumReduceDim = 1;
 // dbeta = reduce_sum(dy, axis=0)
 
 // [CAUSION]
-// In DeviceNormalizationBwdGammaBetaImpl, M is invarient dimension, K is reduced dimension
-// Hence, M in this example and DeviceNormalizationBwdGammaBetaImpl is different
+// In DeviceNormalizationBwdDataImpl & DeviceNormalizationBwdGammaBetaImpl, M is Invariant
+// dimension, K is reduced dimension Hence, M in this example and
+// DeviceNormalizationBwdGammaBetaImpl is different
+using XDeviceInstance = ck::tensor_operation::device::DeviceNormalizationBwdDataImpl<
+    DYDataType,
+    XDataType,
+    GammaDataType,
+    MeanInvStdDataType,
+    ComputeDataType,
+    DXDataType,
+    Rank,
+    NumReduceDim,
+    256,   // BlockSize
+    8,     // MThreadClusterSize
+    32,    // KThreadClusterSize
+    1,     // MThreadSliceSize
+    4,     // KThreadSliceSize
+    true,  // IsDYFastestDimReduced
+    4,     // DYSrcVectorSize
+    true,  // IsXFastestDimReduced
+    4,     // XSrcVectorSize
+    true,  // IsGammaFastestDimReduced
+    4,     // GammaSrcVectorSize
+    false, // IsMeanInvStdFastestDimReduced
+    1,     // MeanInvStdSrcVectorSize
+    true,  // IsDXFastestDimReduced
+    4>;    // DXDstVectorSize
+
 using GammaBetaDeviceInstance = ck::tensor_operation::device::DeviceNormalizationBwdGammaBetaImpl<
     DYDataType,
     XDataType,
@@ -58,18 +86,18 @@ using GammaBetaDeviceInstance = ck::tensor_operation::device::DeviceNormalizatio
     Rank,
     NumReduceDim,
     256,   // BlockSize
-    8,     // ClusterInvarient
-    32,    // ClusterReduce
-    8,     // SliceInvarient
-    1,     // SliceReduce
+    8,     // MThreadClusterSize
+    32,    // KThreadClusterSize
+    4,     // MThreadSliceSize
+    1,     // KThreadSliceSize
     false, // IsDYFastestDimReduced
-    8,     // DYSrcVectorSize
+    4,     // DYSrcVectorSize
     false, // IsXFastestDimReduced
-    8,     // XSrcVectorSize
+    4,     // XSrcVectorSize
     true,  // IsMeanInvStdFastestDimReduced
     1,     // MeanInvStdSrcVectorSize
-    1,     // DGammaDstVectorSize
-    1>;    // DBetaDstVectorSize
+    4,     // DGammaDstVectorSize
+    4>;    // DBetaDstVectorSize
 
 int main()
 {
@@ -96,16 +124,48 @@ int main()
 
     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());
     DeviceMem dgamma_dev(sizeof(DGammaDataType) * dgamma.mDesc.GetElementSpaceSize());
     DeviceMem dbeta_dev(sizeof(DBetaDataType) * dbeta.mDesc.GetElementSpaceSize());
 
     dy_dev.ToDevice(dy.mData.data());
     x_dev.ToDevice(x.mData.data());
+    gamma_dev.ToDevice(gamma.mData.data());
     mean_dev.ToDevice(mean.mData.data());
     inv_std_dev.ToDevice(inv_std.mData.data());
 
+    // backward x
+    auto x_device_instance = XDeviceInstance{};
+
+    auto x_argument_ptr = x_device_instance.MakeArgumentPointer({M, N}, // lengths
+                                                                {N, 1}, // dyStrides
+                                                                {N, 1}, // xStrides
+                                                                {0, 1}, // gammaStrides
+                                                                {1, 0}, // meanStrides
+                                                                {1, 0}, // invStdStrides
+                                                                {N, 1}, // dxStrides
+                                                                {1},    // reduceDims
+                                                                dy_dev.GetDeviceBuffer(),
+                                                                x_dev.GetDeviceBuffer(),
+                                                                gamma_dev.GetDeviceBuffer(),
+                                                                mean_dev.GetDeviceBuffer(),
+                                                                inv_std_dev.GetDeviceBuffer(),
+                                                                dx_dev.GetDeviceBuffer());
+
+    if(!x_device_instance.IsSupportedArgument(x_argument_ptr.get()))
+    {
+        std::cout << "The runtime parameters are not supported." << __FILE__ << ":" << __LINE__
+                  << std::endl;
+        return 1;
+    };
+
+    auto x_invoker_ptr = x_device_instance.MakeInvokerPointer();
+    x_invoker_ptr->Run(x_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
+
+    // backward gamma & beta
     auto gamma_beta_device_instance = GammaBetaDeviceInstance{};
     auto gamma_beta_argument_ptr =
         gamma_beta_device_instance.MakeArgumentPointer({M, N}, // inLengths
@@ -126,7 +186,8 @@ int main()
 
     if(!gamma_beta_device_instance.IsSupportedArgument(gamma_beta_argument_ptr.get()))
     {
-        std::cout << "The runtime parameters are not supported" << std::endl;
+        std::cout << "The runtime parameters are not supported." << __FILE__ << ":" << __LINE__
+                  << std::endl;
         return 1;
     };
 
@@ -156,9 +217,11 @@ int main()
 
         dgamma_dev.FromDevice(dgamma.mData.data());
         dbeta_dev.FromDevice(dbeta.mData.data());
+        dx_dev.FromDevice(dx.mData.data());
 
         pass &= ck::utils::check_err(dgamma, host_dgamma, "Error: Incorrect dgamma", 1e-3, 1e-3);
         pass &= ck::utils::check_err(dbeta, host_dbeta, "Error: Incorrect dbeta", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(dx, host_dx, "Error: Incorrect dx", 1e-3, 1e-3);
     }
 
     return (pass ? 0 : 1);

example/53_layernorm_bwd/CMakeLists.txt

-add_example_executable(example_layernorm2d_bwd_fp16 layernorm2d_bwd_fp16.cpp)

example/54_groupnorm_bwd/CMakeLists.txt

-add_example_executable(example_groupnorm_bwd_fp16 groupnorm_bwd_fp16.cpp)
+add_example_executable(example_groupnorm_bwd_fp32 groupnorm_bwd_fp32.cpp)

example/54_groupnorm_bwd/groupnorm_bwd_fp16.cpp → example/54_groupnorm_bwd/groupnorm_bwd_fp32.cpp

@@ -15,23 +15,58 @@
 #include "ck/library/utility/literals.hpp"
 
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_normalization_bwd_data_impl.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_normalization_bwd_gamma_beta_impl.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_groupnorm_bwd.hpp"
 
-using DYDataType         = ck::half_t;
-using XDataType          = ck::half_t;
-using GammaDataType      = ck::half_t;
+using DYDataType         = float;
+using XDataType          = float;
+using GammaDataType      = float;
 using MeanInvStdDataType = float;
-using DGammaDataType     = ck::half_t;
-using DBetaDataType      = ck::half_t;
-using DXDataType         = ck::half_t;
+using DGammaDataType     = float;
+using DBetaDataType      = float;
+using DXDataType         = float;
 using ComputeDataType    = float;
 
 constexpr int Rank         = 5;
 constexpr int NumReduceDim = 3;
 
 // Grouprnorm
-// kernel:                  M    , K
+// kernel 1:                  M    , K
+// dy:     N, H, W, G, C -> N * G, H * W * C
+// x:      N, H, W, G, C -> N * G, H * W * C
+// gamma:  1, 1, 1, G, C -> 1 * G, 1 * 1 * C
+// mean:   N, 1, 1, G, 1 -> N * G, 1 * 1 * 1
+// rstd:   N, 1, 1, G, 1 -> N * G, 1 * 1 * 1
+
+// dx:     N, H, W, G, C -> N * G, H * W * C
+
+using XDeviceInstance = ck::tensor_operation::device::DeviceNormalizationBwdDataImpl<
+    DYDataType,
+    XDataType,
+    GammaDataType,
+    MeanInvStdDataType,
+    ComputeDataType,
+    DXDataType,
+    Rank,
+    NumReduceDim,
+    256,   // BlockSize
+    8,     // MThreadClusterSize
+    32,    // KThreadClusterSize
+    1,     // MThreadSliceSize
+    4,     // KThreadSliceSize
+    true,  // IsDYFastestDimReduced
+    4,     // DYSrcVectorSize
+    true,  // IsXFastestDimReduced
+    4,     // XSrcVectorSize
+    true,  // IsGammaFastestDimReduced
+    4,     // GammaSrcVectorSize
+    false, // IsMeanInvStdFastestDimReduced
+    1,     // MeanInvStdSrcVectorSize
+    true,  // IsDXFastestDimReduced
+    4>;    // DXDstVectorSize
+
+// kernel 2:                  M    , K
 // dy:     N, H, W, G, C -> G * C, N * H * W
 // x:      N, H, W, G, C -> G * C, N * H * W
 // mean:   N, 1, 1, G, 1 -> G * 1, N * 1 * 1
@@ -52,18 +87,18 @@ using GammaBetaDeviceInstance = ck::tensor_operation::device::DeviceNormalizatio
     Rank,
     NumReduceDim,
     256,   // BlockSize
-    8,     // ClusterInvarient
+    8,     // ClusterInvariant
     32,    // ClusterReduce
-    8,     // SliceInvarient
+    4,     // SliceInvariant
     1,     // SliceReduce
     false, // IsDYFastestDimReduced
-    8,     // DYSrcVectorSize
+    4,     // DYSrcVectorSize
     false, // IsXFastestDimReduced
-    8,     // XSrcVectorSize
+    4,     // XSrcVectorSize
     false, // IsMeanInvStdFastestDimReduced
     1,     // MeanInvStdSrcVectorSize
-    1,     // DGammaDstVectorSize
-    1>;    // DBetaDstVectorSize
+    4,     // DGammaDstVectorSize
+    4>;    // DBetaDstVectorSize
 
 int main()
 {
@@ -93,20 +128,55 @@ int main()
 
     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());
     DeviceMem dgamma_dev(sizeof(DGammaDataType) * dgamma.mDesc.GetElementSpaceSize());
     DeviceMem dbeta_dev(sizeof(DBetaDataType) * dbeta.mDesc.GetElementSpaceSize());
 
     dy_dev.ToDevice(dy.mData.data());
     x_dev.ToDevice(x.mData.data());
+    gamma_dev.ToDevice(gamma.mData.data());
     mean_dev.ToDevice(mean.mData.data());
     inv_std_dev.ToDevice(inv_std.mData.data());
 
     std::vector<ck::index_t> dyStrides{dy.mDesc.GetStrides().begin(), dy.mDesc.GetStrides().end()};
     std::vector<ck::index_t> xStrides{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()};
+    std::vector<ck::index_t> gammaStrides  = {0, 0, 0, C, 1};
     std::vector<ck::index_t> meanStrides   = {G, 0, 0, 1, 0};
     std::vector<ck::index_t> invStdStrides = {G, 0, 0, 1, 0};
+    std::vector<ck::index_t> dxStrides{dx.mDesc.GetStrides().begin(), dx.mDesc.GetStrides().end()};
+
+    // backward x
+    auto x_device_instance = XDeviceInstance{};
+
+    auto x_argument_ptr = x_device_instance.MakeArgumentPointer({N, H, W, G, C}, // lengths
+                                                                dyStrides,       // dyStrides
+                                                                xStrides,        // xStrides
+                                                                gammaStrides,    // gammaStrides
+                                                                meanStrides,     // meanStrides
+                                                                invStdStrides,   // invStdStrides
+                                                                dxStrides,       // dxStrides
+                                                                {1, 2, 4},       // reduceDims
+                                                                dy_dev.GetDeviceBuffer(),
+                                                                x_dev.GetDeviceBuffer(),
+                                                                gamma_dev.GetDeviceBuffer(),
+                                                                mean_dev.GetDeviceBuffer(),
+                                                                inv_std_dev.GetDeviceBuffer(),
+                                                                dx_dev.GetDeviceBuffer());
+
+    if(!x_device_instance.IsSupportedArgument(x_argument_ptr.get()))
+    {
+        std::cout << "The runtime parameters are not supported." << __FILE__ << ":" << __LINE__
+                  << std::endl;
+        return 1;
+    };
+
+    auto x_invoker_ptr = x_device_instance.MakeInvokerPointer();
+    x_invoker_ptr->Run(x_argument_ptr.get(), StreamConfig{nullptr, time_kernel});
+
+    // backward gamma & beta
 
     auto gamma_beta_device_instance = GammaBetaDeviceInstance{};
     auto gamma_beta_argument_ptr =
@@ -128,7 +198,8 @@ int main()
 
     if(!gamma_beta_device_instance.IsSupportedArgument(gamma_beta_argument_ptr.get()))
     {
-        std::cout << "The runtime parameters are not supported" << std::endl;
+        std::cout << "The runtime parameters are not supported." << __FILE__ << ":" << __LINE__
+                  << std::endl;
         return 1;
     };
 
@@ -158,9 +229,11 @@ int main()
 
         dgamma_dev.FromDevice(dgamma.mData.data());
         dbeta_dev.FromDevice(dbeta.mData.data());
+        dx_dev.FromDevice(dx.mData.data());
 
         pass &= ck::utils::check_err(dgamma, host_dgamma, "Error: Incorrect dgamma", 1e-3, 1e-3);
         pass &= ck::utils::check_err(dbeta, host_dbeta, "Error: Incorrect dbeta", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(dx, host_dx, "Error: Incorrect dx", 1e-3, 1e-3);
     }
 
     return (pass ? 0 : 1);

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <vector>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+template <typename DYDataType,
+          typename XDataType,
+          typename GammaDataType,
+          typename MeanInvStdDataType,
+          typename DXDataType,
+          index_t Rank,
+          index_t NumReduceDim>
+struct DeviceNormalizationBwdData : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const std::vector<index_t> lengths,
+                        const std::vector<index_t> dyStrides,
+                        const std::vector<index_t> xStrides,
+                        const std::vector<index_t> gammaStrides,
+                        const std::vector<index_t> meanStrides,
+                        const std::vector<index_t> invStdStrides,
+                        const std::vector<index_t> dxStrides,
+                        const std::vector<index_t> reduceDims,
+                        const void* p_dy,
+                        const void* p_x,
+                        const void* p_gamma,
+                        const void* p_mean,
+                        const void* p_invStd,
+                        void* p_dx) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+template <typename DYDataType,
+          typename XDataType,
+          typename GammaDataType,
+          typename MeanInvStdDataType,
+          typename DXDataType,
+          index_t Rank,
+          index_t NumReduceDim>
+using DeviceNormalizationBwdDataPtr = std::unique_ptr<DeviceNormalizationBwdData<DYDataType,
+                                                                                 XDataType,
+                                                                                 GammaDataType,
+                                                                                 MeanInvStdDataType,
+                                                                                 DXDataType,
+                                                                                 Rank,
+                                                                                 NumReduceDim>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

@@ -14,7 +14,7 @@
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
 
-// M is invarient dimension, K is reduced dimension
+// M is Invariant dimension, K is reduced dimension
 namespace ck {
 namespace tensor_operation {
 namespace device {
@@ -87,7 +87,6 @@ struct DeviceNormalizationBwdGammaBetaImpl
                                              Rank,
                                              NumReduceDim>
 {
-
     static constexpr index_t DYSrcVectorDim         = IsDYFastestDimReduced ? 1 : 0;
     static constexpr index_t XSrcVectorDim          = IsXFastestDimReduced ? 1 : 0;
     static constexpr index_t MeanInvStdSrcVectorDim = IsMeanInvStdFastestDimReduced ? 1 : 0;
@@ -102,18 +101,18 @@ struct DeviceNormalizationBwdGammaBetaImpl
                    (XSrcVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0)),
                   "Invalid thread slice sizes and/or x vector sizes configuration, please check!");
 
-    static_assert(
-        ((MThreadSliceSize % DGammaDstVectorSize == 0) ||
-         (MThreadSliceSize % DBetaDstVectorSize == 0)),
-        "Invalid thread slice sizes and/or Gamma and beta vector sizes configuration, please "
-        "check!");
-
     static_assert(
         (MeanInvStdSrcVectorDim == 0 && MThreadSliceSize % MeanInvStdSrcVectorSize == 0) ||
             (MeanInvStdSrcVectorDim == 1 && KThreadSliceSize % MeanInvStdSrcVectorSize == 0),
         "Invalid thread slice sizes and/or mean and inverse std vector sizes configuration, please "
         "check!");
 
+    static_assert(
+        ((MThreadSliceSize % DGammaDstVectorSize == 0) ||
+         (MThreadSliceSize % DBetaDstVectorSize == 0)),
+        "Invalid thread slice sizes and/or Gamma and beta vector sizes configuration, please "
+        "check!");
+
     static constexpr index_t NumInvariantDim = Rank - NumReduceDim;
     static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
     static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
@@ -298,7 +297,7 @@ struct DeviceNormalizationBwdGammaBetaImpl
         GridDesc_M dgamma_grid_desc_m_;
         GridDesc_M dbeta_grid_desc_m_;
 
-        index_t MRaw_; // invarient length
+        index_t MRaw_; // Invariant length
         index_t KRaw_; // reduce length
     };
 
@@ -457,6 +456,21 @@ struct DeviceNormalizationBwdGammaBetaImpl
     {
         return std::make_unique<Invoker>();
     }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceNormalizationBwdGammaBetaImpl<" << BlockSize << ",";
+        str << "Cluster_MK_" << MThreadClusterSize << "_" << KThreadClusterSize << ",";
+        str << "Slice_MK_" << MThreadSliceSize << "_" << KThreadSliceSize << ",";
+        str << "VectorSize_DY" << DYSrcVectorSize << "_X" << XSrcVectorSize ;
+        str << "_DGamma" << DGammaDstVectorSize << "_DBeta" << DBetaDstVectorSize << ">";
+        // clang-format on
+
+        return str.str();
+    }
 };
 
 } // namespace device

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

@@ -19,7 +19,7 @@ namespace tensor_operation {
 namespace device {
 
 // Y = Normalization(X, Beta, Gamma)
-// M: Invarient length
+// M: Invariant length
 // K: Reduce length (Calculate mean and variance along K dimension)
 // eg. Length = [N, C, H, W], reduce dim = [C, H, W]
 // Then, M = N, K = C * H * W
@@ -263,7 +263,7 @@ struct DeviceNormalizationFwdImpl : public DeviceNormalizationFwd<XDataType,
         GridDesc_M save_inv_std_grid_desc_m_;
         bool isSweeponce_;
 
-        index_t MRaw_; // invarient length
+        index_t MRaw_; // Invariant length
         index_t KRaw_; // reduce length
 
         index_t invariant_lowest_length_;
@@ -342,8 +342,6 @@ struct DeviceNormalizationFwdImpl : public DeviceNormalizationFwd<XDataType,
             }
             else
             {
-                printf("!!!! %d\n", p_arg_->invariant_lowest_length_);
-
                 if(p_arg_->xStrides_[NumInvariantDim - 1] != 1)
                     return false;
 

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

@@ -108,7 +108,7 @@ namespace tensor_operation {
 namespace device {
 
 // Y = Normalization(X, Beta, Gamma)
-// M: Invarient length
+// M: Invariant length
 // K: Reduce length (Calculate mean and variance along K dimension)
 // eg. Length = [N, C, H, W], reduce dim = [C, H, W]
 // Then, M = N, K = C * H * W
@@ -468,7 +468,7 @@ struct DeviceNormalizationFwdSplitKImpl : public DeviceNormalizationFwd<XDataTyp
         Kernel2MeanVarGridDesc_M_KBlock kernel2_mean_var_grid_desc_m_kblock_;
         Kernel2CountGridDesc_M_KBlock kernel2_count_grid_desc_m_kblock_;
 
-        index_t MRaw_; // invarient length
+        index_t MRaw_; // Invariant length
         index_t KRaw_; // reduce length
 
         index_t invariant_lowest_length_;

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

@@ -35,7 +35,7 @@ template <typename DYDataType,
           index_t DBetaDstVectorSize>
 struct GridwiseNormalizationBwdGammaBeta_mk_to_k
 {
-    // if we just check ThreadSliceSize  & VectorSize == 0, the performance may be poor
+    // if we just check ThreadSliceSize % VectorSize == 0, the performance may be poor (coalesce)
     static_assert(((DYSrcVectorDim == 0 && MThreadSliceSize == DYSrcVectorSize) ||
                    (DYSrcVectorDim == 1 && KThreadSliceSize == DYSrcVectorSize)),
                   "Invalid thread slice sizes and/or dy vector sizes configuration, please check!");
@@ -44,6 +44,15 @@ struct GridwiseNormalizationBwdGammaBeta_mk_to_k
                    (XSrcVectorDim == 1 && KThreadSliceSize == XSrcVectorSize)),
                   "Invalid thread slice sizes and/or x vector sizes configuration, please check!");
 
+    // do not force SliceSize == MeanInvStdSrcVectorSize for groupnorm
+    static_assert(
+        ((MeanInvStdSrcVectorDim == 0 && MThreadSliceSize % MeanInvStdSrcVectorSize == 0) ||
+         (MeanInvStdSrcVectorDim == 1 && KThreadSliceSize % MeanInvStdSrcVectorSize == 0)),
+        "Invalid thread slice sizes and/or mean/inv_std vector sizes configuration, please check!");
+
+    static_assert(MThreadSliceSize == DGammaDstVectorSize && MThreadSliceSize == DBetaDstVectorSize,
+                  "Invalid thread slice sizes and/or dx vector sizes configuration, please check!");
+
     using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
 
     using DYThreadBufferDimAccessOrder =

library/include/ck/library/reference_tensor_operation/cpu/reference_groupnorm_bwd.hpp

@@ -16,6 +16,31 @@ namespace ck {
 namespace tensor_operation {
 namespace host {
 
+// def normalization_backward_x(dy, x, gamma, x_mean, rstd, reduce_axis, reduce_size):
+//     ds = np.sum(dy * gamma * x, axis=reduce_axis, keepdims=True)
+//     db = np.sum(dy * gamma, axis=reduce_axis, keepdims=True)
+//     b = (db * x_mean - ds) * rstd ** (3) / reduce_size
+//     c = -b * x_mean - db * rstd / reduce_size
+//     dx = rstd * dy * gamma + b * x + c
+//     return dx
+
+// def normalization_backward_gamma_beta(dy, x, x_mean, rstd, reduce_axis):
+//     # Assume shape of gamma and beta are the same
+//     dgamma = np.sum(dy * (x - x_mean) * rstd, axis=reduce_axis, keepdims=True)
+//     dbeta = np.sum(dy, axis=reduce_axis, keepdims=True)
+//     return dgamma, dbeta
+
+// def groupnorm_backward(dy, x, gamma, x_mean, rstd):
+//     # dy, x = [N, H, W, G, C], gamma = [1, 1, 1, G, C], x_mean, rstd = [N, 1, 1, G, 1]
+//     N, H, W, G, C = x.shape
+//     dx = normalization_input_backward(
+//         dy, x, gamma, x_mean, rstd, (1, 2, 4), H * W * C)
+//     dgamma, dbeta = normalization_gamma_beta_backward(
+//         dy, x, x_mean, rstd, (0, 1, 2))
+//     return dx, dgamma, dbeta
+
+// Reference (Layernorm and groupnorm):
+// https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/cpu/group_norm_kernel.cpp#L655
 template <typename DYDataType,
           typename XDataType,
           typename GammaDataType,

library/include/ck/library/reference_tensor_operation/cpu/reference_layernorm_bwd.hpp

@@ -16,6 +16,30 @@ namespace ck {
 namespace tensor_operation {
 namespace host {
 
+// def normalization_backward_x(dy, x, gamma, x_mean, rstd, reduce_axis, reduce_size):
+//     ds = np.sum(dy * gamma * x, axis=reduce_axis, keepdims=True)
+//     db = np.sum(dy * gamma, axis=reduce_axis, keepdims=True)
+//     b = (db * x_mean - ds) * rstd ** (3) / reduce_size
+//     c = -b * x_mean - db * rstd / reduce_size
+//     dx = rstd * dy * gamma + b * x + c
+//     return dx
+
+// def normalization_beta_backward_gamma_beta(dy, x, x_mean, rstd, reduce_axis):
+//     # Assume shape of gamma and beta are the same
+//     dgamma = np.sum(dy * (x - x_mean) * rstd, axis=reduce_axis, keepdims=True)
+//     dbeta = np.sum(dy, axis=reduce_axis, keepdims=True)
+//     return dgamma, dbeta
+
+// def layernorm_backward(dy, x, gamma, x_mean, rstd):
+//     # dy, x = [M, K], gamma = [1, K], x_mean, rstd = [M, 1]
+//     # dx = [M, K], dgamma, dbeta = [1, K]
+//     M, K = x.shape
+//     dx = normalization_input_backward(dy, x, gamma, x_mean, rstd, 1, K)
+//     dgamma, dbeta = normalization_gamma_beta_backward(dy, x, x_mean, rstd, 0)
+//     return dx, dgamma, dbeta
+
+// Reference (Layernorm and groupnorm):
+// https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/cpu/layer_norm_kernel.cpp#L196
 template <typename DYDataType,
           typename XDataType,
           typename GammaDataType,

library/include/ck/library/tensor_operation_instance/gpu/groupnorm_bwd_data.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+#include <memory>
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_normalization_bwd_data.hpp"
+
+#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+#ifdef CK_ENABLE_FP32
+// FP32
+void add_device_groupnorm_bwd_data_f32_instances(
+    std::vector<std::unique_ptr<DeviceNormalizationBwdData<F32, F32, F32, F32, F32, 5, 3>>>&);
+#endif
+template <typename DYDataType,
+          typename XDataType,
+          typename GammaDataType,
+          typename MeanInvStdDataType,
+          typename DXDataType>
+struct DeviceOperationInstanceFactory<
+    ck::tensor_operation::device::DeviceNormalizationBwdData<DYDataType,
+                                                             XDataType,
+                                                             GammaDataType,
+                                                             MeanInvStdDataType,
+                                                             DXDataType,
+                                                             5,
+                                                             3>>
+{
+    using DeviceOp = DeviceNormalizationBwdData<DYDataType,
+                                                XDataType,
+                                                GammaDataType,
+                                                MeanInvStdDataType,
+                                                DXDataType,
+                                                5,
+                                                3>;
+
+    static auto GetInstances()
+    {
+        std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
+
+#ifdef CK_ENABLE_FP32
+        if constexpr(is_same_v<DYDataType, F32> && is_same_v<XDataType, F32> &&
+                     is_same_v<GammaDataType, F32> && is_same_v<MeanInvStdDataType, F32> &&
+                     is_same_v<DXDataType, F32>)
+        {
+            add_device_groupnorm_bwd_data_f32_instances(op_ptrs);
+        }
+#endif
+        return op_ptrs;
+    }
+};
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck