Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/composable_kernel into lwpck-977

include/ck/tensor_operation/gpu/warp/xdlops_gemm.hpp

@@ -462,7 +462,6 @@ struct mfma_type<MfmaInstr::mfma_f64_16x16x4f64>
     }
 };
 
-#if defined CK_ENABLE_FP8
 template <>
 struct mfma_type<MfmaInstr::mfma_f32_32x32x16f8f8>
 {
@@ -506,9 +505,7 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32f8f8>
         intrin_mfma_f32_16x16x32f8f8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
     }
 };
-#endif
 
-#if defined CK_ENABLE_BF8
 template <>
 struct mfma_type<MfmaInstr::mfma_f32_32x32x16bf8bf8>
 {
@@ -552,9 +549,7 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32bf8bf8>
         intrin_mfma_f32_16x16x32bf8bf8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
     }
 };
-#endif
 
-#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
 template <>
 struct mfma_type<MfmaInstr::mfma_f32_32x32x16f8bf8>
 {
@@ -598,9 +593,7 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32f8bf8>
         intrin_mfma_f32_16x16x32f8bf8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
     }
 };
-#endif
 
-#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
 template <>
 struct mfma_type<MfmaInstr::mfma_f32_32x32x16bf8f8>
 {
@@ -644,7 +637,6 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32bf8f8>
         intrin_mfma_f32_16x16x32bf8f8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
     }
 };
-#endif
 
 template <typename base_type,
           index_t MPerXdlops,
@@ -792,7 +784,6 @@ struct MfmaSelector
     }
 #endif
 
-#if defined CK_ENABLE_FP8
     template <>
     static constexpr auto GetMfma<f8_t, 32, 32>()
     {
@@ -804,9 +795,7 @@ struct MfmaSelector
     {
         return MfmaInstr::mfma_f32_16x16x32f8f8;
     }
-#endif
 
-#if defined CK_ENABLE_BF8
     template <>
     static constexpr auto GetMfma<bf8_t, 32, 32>()
     {
@@ -818,9 +807,7 @@ struct MfmaSelector
     {
         return MfmaInstr::mfma_f32_16x16x32bf8bf8;
     }
-#endif
 
-#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
     template <>
     static constexpr auto GetMfma<f8_t, 32, 32, bf8_t>()
     {
@@ -832,9 +819,7 @@ struct MfmaSelector
     {
         return MfmaInstr::mfma_f32_16x16x32f8bf8;
     }
-#endif
 
-#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
     template <>
     static constexpr auto GetMfma<bf8_t, 32, 32, f8_t>()
     {
@@ -846,7 +831,6 @@ struct MfmaSelector
     {
         return MfmaInstr::mfma_f32_16x16x32bf8f8;
     }
-#endif
 
     static constexpr auto selected_mfma =
         mfma_type<GetMfma<base_type, MPerXdlops, NPerXdlops, additional_type>()>{};
@@ -1051,18 +1035,10 @@ struct XdlopsGemm
         static_assert(
             is_same<base_type, double>::value || is_same<base_type, float>::value ||
                 is_same<base_type, half_t>::value || is_same<base_type, bhalf_t>::value ||
-                is_same<base_type, int8_t>::value
-#if defined CK_ENABLE_FP8
-                || is_same<base_type, f8_t>::value
-#endif
-#if defined CK_ENABLE_BF8
-                || is_same<base_type, bf8_t>::value
-#endif
-#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
-                || (is_same<base_type, f8_t>::value && is_same<additional_type, bf8_t>::value) ||
-                (is_same<base_type, bf8_t>::value && is_same<additional_type, f8_t>::value)
-#endif
-                ,
+                is_same<base_type, int8_t>::value || is_same<base_type, f8_t>::value ||
+                is_same<base_type, bf8_t>::value ||
+                (is_same<base_type, f8_t>::value && is_same<additional_type, bf8_t>::value) ||
+                (is_same<base_type, bf8_t>::value && is_same<additional_type, f8_t>::value),
             "base base_type must be double, float, half, bfloat16, int8_t, f8_t or bf8_t!");
 
         static_for<0, KPack / mfma_instr.k_per_blk, 1>{}([&](auto k) {

include/ck/utility/amd_xdlops.hpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
-#ifndef CK_AMD_XDLOPS_HPP
-#define CK_AMD_XDLOPS_HPP
-
-#include "data_type.hpp"
+#pragma once
 
 namespace ck {
 
@@ -355,7 +352,6 @@ struct intrin_mfma_f64_16x16x4f64<16, 16>
     }
 };
 
-#if defined CK_ENABLE_FP8
 template <index_t MPerWave, index_t NPerWave>
 struct intrin_mfma_f32_32x32x16f8f8;
 
@@ -418,9 +414,7 @@ struct intrin_mfma_f32_16x16x32f8f8<16, 16>
 #endif
     }
 };
-#endif
 
-#if defined CK_ENABLE_BF8
 template <index_t MPerWave, index_t NPerWave>
 struct intrin_mfma_f32_32x32x16bf8bf8;
 
@@ -483,9 +477,7 @@ struct intrin_mfma_f32_16x16x32bf8bf8<16, 16>
 #endif
     }
 };
-#endif
 
-#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
 template <index_t MPerWave, index_t NPerWave>
 struct intrin_mfma_f32_32x32x16f8bf8;
 
@@ -548,9 +540,7 @@ struct intrin_mfma_f32_16x16x32f8bf8<16, 16>
 #endif
     }
 };
-#endif
 
-#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
 template <index_t MPerWave, index_t NPerWave>
 struct intrin_mfma_f32_32x32x16bf8f8;
 
@@ -613,6 +603,5 @@ struct intrin_mfma_f32_16x16x32bf8f8<16, 16>
 #endif
     }
 };
-#endif
+
 } // namespace ck
-#endif

include/ck/utility/data_type.hpp

@@ -9,11 +9,9 @@ namespace ck {
 
 using bhalf_t = ushort;
 using half_t  = _Float16;
-#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-using int4_t = _BitInt(4);
-#endif
-using f8_t  = _BitInt(8);
-using bf8_t = unsigned _BitInt(8);
+using int4_t  = _BitInt(4);
+using f8_t    = _BitInt(8);
+using bf8_t   = unsigned _BitInt(8);
 
 // vector_type
 template <typename T, index_t N>
@@ -1123,5 +1121,4 @@ struct NumericUtils<bf8_t>
     static constexpr int bias = 16; // negative zero nan mode
     // static constexpr int bias = 15; // ieee mode
 };
-
 } // namespace ck

include/ck/utility/inner_product.hpp

@@ -192,6 +192,8 @@ inner_product<int8x4_t, int8x4_t, int32_t>(const int8x4_t& a, const int8x4_t& b,
 #else
     c = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b), c, false);
 #endif
+#elif defined(CK_USE_AMD_V_DOT4_I32_I8_GFX11)
+    c = __builtin_amdgcn_sudot4(true, bit_cast<int32_t>(a), true, bit_cast<int32_t>(b), c, false);
 #else
     const vector_type<int8_t, 4> a_vector{a};
     const vector_type<int8_t, 4> b_vector{b};

include/ck/utility/math.hpp

@@ -150,28 +150,6 @@ __host__ __device__ constexpr T clamp(const T& x, const T& lowerbound, const T&
     return min(max(x, lowerbound), upperbound);
 }
 
-// disallow implicit type casting
-template <typename T>
-__device__ T exp(T x);
-
-// TODO: add f16 support using v_exp_f16
-
-template <>
-__device__ float exp<float>(float x)
-{
-    return __expf(x);
-}
-
-template <>
-__device__ double exp<double>(double x)
-{
-    return exp(x);
-}
-
-static inline __host__ float exp(float x) { return std::expf(x); }
-
-static inline __host__ double exp(double x) { return std::exp(x); }
-
 // greatest common divisor, aka highest common factor
 __host__ __device__ constexpr index_t gcd(index_t x, index_t y)
 {

include/ck/utility/math_v2.hpp

@@ -9,6 +9,7 @@
 
 #include "ck/utility/data_type.hpp"
 #include "ck/utility/type.hpp"
+#include "ck/utility/type_convert.hpp"
 
 namespace ck {
 namespace math {
@@ -92,14 +93,96 @@ static inline __host__ float sqrt(float x) { return std::sqrt(x); };
 
 static inline __host__ double sqrt(double x) { return std::sqrt(x); };
 
-static inline __host__ half_t tanh(half_t x)
+template <typename T>
+inline __host__ T tanh(T x)
 {
-    return static_cast<half_t>(std::tanh(static_cast<float>(x)));
+    return ck::type_convert<T>(std::tanhf(ck::type_convert<float>(x)));
 };
 
-static inline __host__ float tanh(float x) { return std::tanh(x); };
+template <>
+inline __host__ float tanh<float>(float x)
+{
+    return std::tanhf(x);
+};
+
+template <>
+inline __host__ double tanh<double>(double x)
+{
+    return std::tanh(x);
+};
+
+template <typename T>
+inline __host__ T exp(T x)
+{
+    return ck::type_convert<T>(std::expf(ck::type_convert<float>(x)));
+}
+
+template <>
+inline __host__ float exp<float>(float x)
+{
+    return std::expf(x);
+}
 
-static inline __host__ double tanh(double x) { return std::tanh(x); };
+template <>
+inline __host__ double exp<double>(double x)
+{
+    return std::exp(x);
+}
+
+template <typename T>
+inline __host__ T log(T x)
+{
+    return ck::type_convert<T>(std::logf(ck::type_convert<float>(x)));
+}
+
+template <>
+inline __host__ float log<float>(float x)
+{
+    return std::logf(x);
+}
+
+template <>
+inline __host__ double log<double>(double x)
+{
+    return std::log(x);
+}
+
+template <typename T>
+inline __host__ T pow(T x, T gamma)
+{
+    return ck::type_convert<T>(
+        std::powf(ck::type_convert<float>(x), ck::type_convert<float>(gamma)));
+}
+
+template <>
+inline __host__ float pow<float>(float x, float gamma)
+{
+    return std::powf(x, gamma);
+}
+
+template <>
+inline __host__ double pow<double>(double x, double gamma)
+{
+    return std::pow(x, gamma);
+}
+
+template <typename T>
+inline __host__ T expm1(T x)
+{
+    return ck::type_convert<T>(std::expm1f(ck::type_convert<float>(x)));
+}
+
+template <>
+inline __host__ float expm1<float>(float x)
+{
+    return std::expm1f(x);
+}
+
+template <>
+inline __host__ double expm1<double>(double x)
+{
+    return std::expm1(x);
+}
 
 // math functions for the HIP kernel,  some are implemented by calling hip builtin functions
 
@@ -181,14 +264,107 @@ static inline __device__ float sqrt(float x) { return __builtin_amdgcn_sqrtf(x);
 
 static inline __device__ double sqrt(double x) { return __builtin_amdgcn_sqrt(x); };
 
-static inline __device__ half_t tanh(half_t x)
+template <typename T>
+inline __device__ T tanh(T x)
+{
+    return ck::type_convert<T>(::tanhf(ck::type_convert<float>(x)));
+};
+
+template <>
+inline __device__ float tanh<float>(float x)
 {
-    return static_cast<half_t>(::tanhf(static_cast<float>(x)));
+    return ::tanhf(x);
 };
 
-static inline __device__ float tanh(float x) { return ::tanhf(x); };
+template <>
+inline __device__ double tanh<double>(double x)
+{
+    return ::tanh(x);
+};
+
+template <typename T>
+inline __device__ T exp(T x)
+{
+    return ck::type_convert<T>(__expf(ck::type_convert<float>(x)));
+};
+
+template <>
+inline __device__ half_t exp<half_t>(half_t x)
+{
+    return hexp(x);
+};
+
+template <>
+inline __device__ float exp<float>(float x)
+{
+    return __expf(x);
+};
 
-static inline __device__ double tanh(double x) { return ::tanh(x); };
+template <>
+inline __device__ double exp<double>(double x)
+{
+    return exp(x);
+};
+
+template <typename T>
+inline __device__ T log(T x)
+{
+    return ck::type_convert<T>(__logf(ck::type_convert<float>(x)));
+};
+
+template <>
+inline __device__ half_t log<half_t>(half_t x)
+{
+    return hlog(x);
+};
+
+template <>
+inline __device__ float log<float>(float x)
+{
+    return __logf(x);
+};
+
+template <>
+inline __device__ double log<double>(double x)
+{
+    return log(x);
+};
+
+template <typename T>
+inline __device__ T pow(T x, T gamma)
+{
+    return ck::type_convert<T>(powf(ck::type_convert<float>(x), ck::type_convert<float>(gamma)));
+};
+
+template <>
+inline __device__ float pow<float>(float x, float gamma)
+{
+    return powf(x, gamma);
+};
+
+template <>
+inline __device__ double pow<double>(double x, double gamma)
+{
+    return pow(x, gamma);
+};
+
+template <typename T>
+inline __device__ T expm1(T x)
+{
+    return ck::type_convert<T>(expm1f(ck::type_convert<float>(x)));
+};
+
+template <>
+inline __device__ float expm1<float>(float x)
+{
+    return expm1f(x);
+};
+
+template <>
+inline __device__ double expm1<double>(double x)
+{
+    return expm1(x);
+};
 
 } // namespace math
 } // namespace ck

include/ck/utility/statically_indexed_array_multi_index.hpp

@@ -5,6 +5,7 @@
 #define CK_STATICALLY_INDEXED_ARRAY_MULTI_INDEX_HPP
 
 #include "common_header.hpp"
+#include "ck/utility/math_v2.hpp"
 
 namespace ck {
 

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

@@ -128,11 +128,9 @@ struct ReferenceConvFwd : public device::BaseOperator
                         }
                     }
 
-                    float v_out;
-
-                    arg.out_element_op_(v_out, v_acc);
-
-                    arg.output_(g, n, k, wo) = ck::type_convert<OutDataType>(v_out);
+                    OutDataType v_out;
+                    arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc));
+                    arg.output_(g, n, k, wo) = v_out;
                 };
 
                 make_ParallelTensorFunctor(func,
@@ -184,11 +182,9 @@ struct ReferenceConvFwd : public device::BaseOperator
                         }
                     }
 
-                    float v_out;
-
-                    arg.out_element_op_(v_out, v_acc);
-
-                    arg.output_(g, n, k, ho, wo) = ck::type_convert<OutDataType>(v_out);
+                    OutDataType v_out;
+                    arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc));
+                    arg.output_(g, n, k, ho, wo) = v_out;
                 };
 
                 make_ParallelTensorFunctor(func,
@@ -253,11 +249,9 @@ struct ReferenceConvFwd : public device::BaseOperator
                         }
                     }
 
-                    float v_out;
-
-                    arg.out_element_op_(v_out, v_acc);
-
-                    arg.output_(g, n, k, d_o, ho, wo) = ck::type_convert<OutDataType>(v_out);
+                    OutDataType v_out;
+                    arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc));
+                    arg.output_(g, n, k, d_o, ho, wo) = v_out;
                 };
 
                 make_ParallelTensorFunctor(func,

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

@@ -20,8 +20,9 @@ template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
-          typename AccDataType,
-          typename AccElementwiseOperation>
+          typename SaveMeanInvStdDataType,
+          typename ComputeDataType,
+          typename YElementwiseOperation>
 struct ReferenceGroupnorm : public device::BaseOperator
 {
     // x = [N, H, W, G, C]
@@ -35,14 +36,18 @@ struct ReferenceGroupnorm : public device::BaseOperator
                  const Tensor<GammaDataType>& gamma,
                  const Tensor<BetaDataType>& beta,
                  Tensor<YDataType>& y,
-                 AccElementwiseOperation acc_elementwise_op,
+                 Tensor<SaveMeanInvStdDataType>& save_mean,
+                 Tensor<SaveMeanInvStdDataType>& save_inv_std,
+                 YElementwiseOperation y_elementwise_op,
                  const std::vector<index_t> lengths,
-                 AccDataType epsilon)
+                 ComputeDataType epsilon)
             : x_(x),
               gamma_(gamma),
               beta_(beta),
               y_(y),
-              acc_elementwise_op_(acc_elementwise_op),
+              save_mean_(save_mean),
+              save_inv_std_(save_inv_std),
+              y_elementwise_op_(y_elementwise_op),
               lengths_(lengths),
               epsilon_(epsilon)
         {
@@ -52,9 +57,11 @@ struct ReferenceGroupnorm : public device::BaseOperator
         const Tensor<XDataType> gamma_;
         const Tensor<XDataType> beta_;
         Tensor<YDataType>& y_;
-        AccElementwiseOperation acc_elementwise_op_;
+        Tensor<SaveMeanInvStdDataType>& save_mean_;
+        Tensor<SaveMeanInvStdDataType>& save_inv_std_;
+        YElementwiseOperation y_elementwise_op_;
         std::vector<index_t> lengths_;
-        AccDataType epsilon_;
+        ComputeDataType epsilon_;
     };
 
     // Invoker
@@ -68,8 +75,8 @@ struct ReferenceGroupnorm : public device::BaseOperator
             int G = arg.lengths_[3];
             int C = arg.lengths_[4];
 
-            Tensor<AccDataType> mean({N, G});
-            Tensor<AccDataType> var({N, G});
+            Tensor<ComputeDataType> mean({N, G});
+            Tensor<ComputeDataType> var({N, G});
 
             // Compute mean & var in [H, W, C] by Welford Algorithm
             // TODO - parallel for each HWC
@@ -78,9 +85,9 @@ struct ReferenceGroupnorm : public device::BaseOperator
             {
                 for(int g = 0; g < G; ++g)
                 {
-                    AccDataType mean_val = type_convert<AccDataType>(0.0f);
-                    AccDataType var_val  = type_convert<AccDataType>(0.0f);
-                    int32_t curr_count   = 0;
+                    ComputeDataType mean_val = type_convert<ComputeDataType>(0.0f);
+                    ComputeDataType var_val  = type_convert<ComputeDataType>(0.0f);
+                    int32_t curr_count       = 0;
 
                     for(int h = 0; h < H; ++h)
                     {
@@ -89,10 +96,11 @@ struct ReferenceGroupnorm : public device::BaseOperator
                             for(int c = 0; c < C; ++c)
                             {
                                 curr_count++;
-                                AccDataType x = type_convert<AccDataType>(arg.x_(n, h, w, g, c));
-                                AccDataType delta = x - mean_val;
+                                ComputeDataType x =
+                                    type_convert<ComputeDataType>(arg.x_(n, h, w, g, c));
+                                ComputeDataType delta = x - mean_val;
                                 mean_val += delta / curr_count;
-                                AccDataType delta2 = x - mean_val;
+                                ComputeDataType delta2 = x - mean_val;
                                 var_val += delta * delta2;
                             }
                         }
@@ -100,6 +108,12 @@ struct ReferenceGroupnorm : public device::BaseOperator
 
                     mean(n, g) = mean_val;
                     var(n, g)  = var_val / curr_count;
+
+                    arg.save_mean_(n, g) = ck::type_convert<SaveMeanInvStdDataType>(mean(n, g));
+
+                    ComputeDataType divisor =
+                        static_cast<ComputeDataType>(1) / ck::math::sqrt(var(n, g) + arg.epsilon_);
+                    arg.save_inv_std_(n, g) = ck::type_convert<SaveMeanInvStdDataType>(divisor);
                 }
             }
 
@@ -114,15 +128,19 @@ struct ReferenceGroupnorm : public device::BaseOperator
                         {
                             for(int c = 0; c < C; ++c)
                             {
-                                AccDataType x = type_convert<AccDataType>(arg.x_(n, h, w, g, c));
-                                AccDataType gamma    = type_convert<AccDataType>(arg.gamma_(g, c));
-                                AccDataType beta     = type_convert<AccDataType>(arg.beta_(g, c));
-                                AccDataType mean_val = type_convert<AccDataType>(mean(n, g));
-                                AccDataType var_val  = type_convert<AccDataType>(var(n, g));
-                                AccDataType y        = gamma * (x - mean_val) /
-                                                    ck::math::sqrt(arg.epsilon_ + var_val) +
-                                                beta;
-                                arg.acc_elementwise_op_(y, y);
+                                ComputeDataType x =
+                                    type_convert<ComputeDataType>(arg.x_(n, h, w, g, c));
+                                ComputeDataType gamma =
+                                    type_convert<ComputeDataType>(arg.gamma_(g, c));
+                                ComputeDataType beta =
+                                    type_convert<ComputeDataType>(arg.beta_(g, c));
+                                ComputeDataType mean_val =
+                                    type_convert<ComputeDataType>(mean(n, g));
+                                ComputeDataType var_val = type_convert<ComputeDataType>(var(n, g));
+                                ComputeDataType y       = gamma * (x - mean_val) /
+                                                        ck::math::sqrt(arg.epsilon_ + var_val) +
+                                                    beta;
+                                arg.y_elementwise_op_(y, y);
                                 arg.y_(n, h, w, g, c) = type_convert<YDataType>(y);
                             }
                         }
@@ -159,11 +177,14 @@ struct ReferenceGroupnorm : public device::BaseOperator
                              const Tensor<GammaDataType>& gamma,
                              const Tensor<BetaDataType>& beta,
                              Tensor<YDataType>& y,
-                             AccElementwiseOperation acc_elementwise_op,
+                             Tensor<SaveMeanInvStdDataType>& save_mean,
+                             Tensor<SaveMeanInvStdDataType>& save_inv_std,
+                             YElementwiseOperation y_elementwise_op,
                              const std::vector<index_t> lengths,
-                             AccDataType epsilon)
+                             ComputeDataType epsilon)
     {
-        return Argument{x, gamma, beta, y, acc_elementwise_op, lengths, epsilon};
+        return Argument{
+            x, gamma, beta, y, save_mean, save_inv_std, y_elementwise_op, lengths, epsilon};
     }
 
     static auto MakeInvoker() { return Invoker{}; }

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

@@ -20,8 +20,9 @@ template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
-          typename AccDataType,
-          typename AccElementwiseOperation,
+          typename SaveMeanInvStdDataType,
+          typename ComputeDataType,
+          typename YElementwiseOperation,
           index_t Rank,
           index_t NumReduceDim>
 struct ReferenceLayernorm : public device::BaseOperator
@@ -36,15 +37,19 @@ struct ReferenceLayernorm : public device::BaseOperator
                  const Tensor<GammaDataType>& gamma_n,
                  const Tensor<BetaDataType>& beta_n,
                  Tensor<YDataType>& y_m_n,
-                 AccElementwiseOperation acc_elementwise_op,
+                 Tensor<SaveMeanInvStdDataType>& save_mean_m,
+                 Tensor<SaveMeanInvStdDataType>& save_inv_std_m,
+                 YElementwiseOperation y_elementwise_op,
                  const std::vector<index_t> lengths,
                  const std::vector<index_t> reduceDims,
-                 AccDataType epsilon)
+                 ComputeDataType epsilon)
             : x_m_n_(x_m_n),
               gamma_n_(gamma_n),
               beta_n_(beta_n),
               y_m_n_(y_m_n),
-              acc_elementwise_op_(acc_elementwise_op),
+              save_mean_m_(save_mean_m),
+              save_inv_std_m_(save_inv_std_m),
+              y_elementwise_op_(y_elementwise_op),
               lengths_(lengths),
               reduceDims_(reduceDims),
               epsilon_(epsilon)
@@ -55,10 +60,12 @@ struct ReferenceLayernorm : public device::BaseOperator
         const Tensor<XDataType> gamma_n_;
         const Tensor<XDataType> beta_n_;
         Tensor<YDataType>& y_m_n_;
-        AccElementwiseOperation acc_elementwise_op_;
+        Tensor<SaveMeanInvStdDataType>& save_mean_m_;
+        Tensor<SaveMeanInvStdDataType>& save_inv_std_m_;
+        YElementwiseOperation y_elementwise_op_;
         std::vector<index_t> lengths_;
         std::vector<index_t> reduceDims_;
-        AccDataType epsilon_;
+        ComputeDataType epsilon_;
     };
 
     // Invoker
@@ -69,8 +76,8 @@ struct ReferenceLayernorm : public device::BaseOperator
             int M = arg.lengths_[0];
             int N = arg.lengths_[1];
 
-            Tensor<AccDataType> mean({M});
-            Tensor<AccDataType> var({M});
+            Tensor<ComputeDataType> mean({M});
+            Tensor<ComputeDataType> var({M});
 
             for(int m = 0; m < M; ++m)
             {
@@ -79,7 +86,7 @@ struct ReferenceLayernorm : public device::BaseOperator
 
                 for(int n = 0; n < N; ++n)
                 {
-                    auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n));
+                    auto x_val = ck::type_convert<ComputeDataType>(arg.x_m_n_(m, n));
                     mean(m) += x_val;
                     var(m) += x_val * x_val;
                 }
@@ -90,17 +97,21 @@ struct ReferenceLayernorm : public device::BaseOperator
 
             for(int m = 0; m < M; ++m)
             {
-                AccDataType divisor =
-                    static_cast<AccDataType>(1) / ck::math::sqrt(var(m) + arg.epsilon_);
+                ComputeDataType divisor =
+                    static_cast<ComputeDataType>(1) / ck::math::sqrt(var(m) + arg.epsilon_);
 
                 for(int n = 0; n < N; ++n)
                 {
-                    auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n));
-                    auto y_val = (x_val - mean(m)) * divisor;
-                    y_val      = (y_val * arg.gamma_n_(n)) + arg.beta_n_(n);
-                    arg.acc_elementwise_op_(y_val, y_val);
+                    auto x_val     = ck::type_convert<ComputeDataType>(arg.x_m_n_(m, n));
+                    auto gamma_val = ck::type_convert<ComputeDataType>(arg.gamma_n_(n));
+                    auto beta_val  = ck::type_convert<ComputeDataType>(arg.beta_n_(n));
+                    auto y_val     = (x_val - mean(m)) * divisor;
+                    y_val          = (y_val * gamma_val) + beta_val;
+                    arg.y_elementwise_op_(y_val, y_val);
                     arg.y_m_n_(m, n) = ck::type_convert<YDataType>(y_val);
                 }
+                arg.save_mean_m_(m)    = ck::type_convert<SaveMeanInvStdDataType>(mean(m));
+                arg.save_inv_std_m_(m) = ck::type_convert<SaveMeanInvStdDataType>(divisor);
             }
 
             return 0;
@@ -140,13 +151,23 @@ struct ReferenceLayernorm : public device::BaseOperator
                              const Tensor<GammaDataType>& gamma_n,
                              const Tensor<BetaDataType>& beta_n,
                              Tensor<YDataType>& y_m_n,
-                             AccElementwiseOperation acc_elementwise_op,
+                             Tensor<SaveMeanInvStdDataType>& save_mean_m,
+                             Tensor<SaveMeanInvStdDataType>& save_inv_std_m,
+                             YElementwiseOperation y_elementwise_op,
                              const std::vector<index_t> lengths,
                              const std::vector<index_t> reduceDims,
-                             AccDataType epsilon)
+                             ComputeDataType epsilon)
     {
-        return Argument{
-            x_m_n, gamma_n, beta_n, y_m_n, acc_elementwise_op, lengths, reduceDims, epsilon};
+        return Argument{x_m_n,
+                        gamma_n,
+                        beta_n,
+                        y_m_n,
+                        save_mean_m,
+                        save_inv_std_m,
+                        y_elementwise_op,
+                        lengths,
+                        reduceDims,
+                        epsilon};
     }
 
     static auto MakeInvoker() { return Invoker{}; }

library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp

@@ -20,12 +20,8 @@ using F16  = ck::half_t;
 using BF16 = ck::bhalf_t;
 using I8   = int8_t;
 using I32  = int32_t;
-#if defined CK_ENABLE_FP8
-using F8 = ck::f8_t;
-#endif
-#if defined CK_ENABLE_BF8
-using BF8 = ck::bf8_t;
-#endif
+using F8   = ck::f8_t;
+using BF8  = ck::bf8_t;
 
 using Empty_Tuple = ck::Tuple<>;
 

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

@@ -240,11 +240,13 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
         if constexpr(NumDimSpatial == 1 && is_same_v<InLayout, NWC> && is_same_v<WeiLayout, KXC> &&
                      is_same_v<OutLayout, NWK>)
         {
+#ifdef CK_ENABLE_FP32
             if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                          is_same_v<OutDataType, float>)
             {
                 add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(op_ptrs);
             }
+#endif
 #ifdef CK_ENABLE_FP16
             if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                          is_same_v<OutDataType, half_t>)
@@ -267,17 +269,23 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
             }
 #endif
         }
-        else if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWC> &&
-                          is_same_v<WeiLayout, KYXC> && is_same_v<OutLayout, NHWK>)
+        if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWC> &&
+                     is_same_v<WeiLayout, KYXC> && is_same_v<OutLayout, NHWK>)
         {
+#ifdef CK_ENABLE_FP32
             if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                          is_same_v<OutDataType, float>)
             {
                 add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
-#ifdef DL_KERNELS
-                add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
+            }
 #endif
+#if defined(DL_KERNELS) && defined(CK_ENABLE_FP32)
+            if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
+                         is_same_v<OutDataType, float>)
+            {
+                add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
             }
+#endif
 #ifdef CK_ENABLE_FP16
             if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                          is_same_v<OutDataType, half_t>)
@@ -306,14 +314,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
             }
 #endif
         }
-        else if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, NDHWC> &&
-                          is_same_v<WeiLayout, KZYXC> && is_same_v<OutLayout, NDHWK>)
+        if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, NDHWC> &&
+                     is_same_v<WeiLayout, KZYXC> && is_same_v<OutLayout, NDHWK>)
         {
+#ifdef CK_ENABLE_FP32
             if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                          is_same_v<OutDataType, float>)
             {
                 add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(op_ptrs);
             }
+#endif
 #ifdef CK_ENABLE_FP16
             if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                          is_same_v<OutDataType, half_t>)

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

@@ -98,30 +98,31 @@ struct DeviceOperationInstanceFactory<
         if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWC> &&
                      is_same_v<WeiLayout, KYXC> && is_same_v<OutLayout, NHWK>)
         {
+#ifdef CK_ENABLE_FP32
             if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                          is_same_v<OutDataType, float>)
             {
                 add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
             }
+#endif
 #ifdef CK_ENABLE_FP16
-            else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                              is_same_v<OutDataType, half_t>)
+            if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                         is_same_v<OutDataType, half_t>)
             {
                 add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(op_ptrs);
                 add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(op_ptrs);
             }
 #endif
 #ifdef CK_ENABLE_BF16
-            else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
-                              is_same_v<WeiDataType, ck::bhalf_t> &&
-                              is_same_v<OutDataType, ck::bhalf_t>)
+            if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
+                         is_same_v<WeiDataType, ck::bhalf_t> && is_same_v<OutDataType, ck::bhalf_t>)
             {
                 add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(op_ptrs);
             }
 #endif
 #ifdef CK_ENABLE_INT8
-            else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
-                              is_same_v<OutDataType, int8_t>)
+            if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
+                         is_same_v<OutDataType, int8_t>)
             {
                 add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(op_ptrs);
             }

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

@@ -155,7 +155,7 @@ struct DeviceOperationInstanceFactory<
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
 #ifdef CK_ENABLE_FP32
         if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
-                     is_same_v<CDataType, float>)
+                     is_same_v<CDataType, float> && is_same_v<ComputeType, float>)
         {
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
@@ -180,8 +180,8 @@ struct DeviceOperationInstanceFactory<
         }
 #endif
 #ifdef CK_ENABLE_FP16
-        else if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
-                          is_same_v<CDataType, half_t> && is_same_v<ComputeType, half_t>)
+        if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
+                     is_same_v<CDataType, half_t> && is_same_v<ComputeType, half_t>)
         {
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
@@ -206,8 +206,8 @@ struct DeviceOperationInstanceFactory<
         }
 #endif
 #if(defined(CK_ENABLE_FP16) || defined(CK_ENABLE_FP8))
-        else if constexpr(is_same_v<ADataType, f8_t> && is_same_v<BDataType, half_t> &&
-                          is_same_v<CDataType, half_t>)
+        if constexpr(is_same_v<ADataType, f8_t> && is_same_v<BDataType, half_t> &&
+                     is_same_v<CDataType, half_t> && is_same_v<ComputeType, half_t>)
         {
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)
@@ -230,8 +230,8 @@ struct DeviceOperationInstanceFactory<
                 add_device_gemm_xdl_splitk_f8_f16_f16_km_nk_mn_instances(op_ptrs);
             }
         }
-        else if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
-                          is_same_v<CDataType, half_t>)
+        if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
+                     is_same_v<CDataType, half_t> && is_same_v<ComputeType, half_t>)
         {
             if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
                          is_same_v<CLayout, Row>)

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

@@ -627,8 +627,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_FP16
-                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                                  is_same_v<OutDataType, half_t>)
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_f16_instances(op_ptrs);
@@ -637,9 +637,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_BF16
-                else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
-                                  is_same_v<WeiDataType, float> &&
-                                  is_same_v<OutDataType, ck::bhalf_t>)
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_bf16_f32_bf16_instances(
@@ -650,8 +649,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
             }
-            else if constexpr(is_same_v<InLayout, NWGC> && is_same_v<WeiLayout, GKXC> &&
-                              is_same_v<OutLayout, NWGK>)
+            if constexpr(is_same_v<InLayout, NWGC> && is_same_v<WeiLayout, GKXC> &&
+                         is_same_v<OutLayout, NWGK>)
             {
 #ifdef DL_KERNELS
 #ifdef CK_ENABLE_FP32
@@ -662,16 +661,15 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_FP16
-                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                                  is_same_v<OutDataType, half_t>)
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t>)
                 {
                     add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_f16_instances(op_ptrs);
                 }
 #endif
 #ifdef CK_ENABLE_BF16
-                else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
-                                  is_same_v<WeiDataType, float> &&
-                                  is_same_v<OutDataType, ck::bhalf_t>)
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t>)
                 {
                     add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_bf16_f32_bf16_instances(
                         op_ptrs);
@@ -680,7 +678,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
 #endif
             }
         }
-        else if constexpr(NumDimSpatial == 2)
+        if constexpr(NumDimSpatial == 2)
         {
             if constexpr(is_same_v<InLayout, GNHWC> && is_same_v<WeiLayout, GKYXC> &&
                          is_same_v<OutLayout, GNHWK>)
@@ -698,8 +696,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_FP16
-                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                                  is_same_v<OutDataType, half_t>)
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_f16_instances(
@@ -710,9 +708,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_BF16
-                else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
-                                  is_same_v<WeiDataType, float> &&
-                                  is_same_v<OutDataType, ck::bhalf_t>)
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances(
@@ -723,8 +720,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
             }
-            else if constexpr(is_same_v<InLayout, NHWGC> && is_same_v<WeiLayout, GKYXC> &&
-                              is_same_v<OutLayout, NHWGK>)
+            if constexpr(is_same_v<InLayout, NHWGC> && is_same_v<WeiLayout, GKYXC> &&
+                         is_same_v<OutLayout, NHWGK>)
             {
 #ifdef CK_ENABLE_FP32
                 if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
@@ -739,8 +736,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_FP16
-                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                                  is_same_v<OutDataType, half_t>)
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_f16_instances(
@@ -751,9 +748,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_BF16
-                else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
-                                  is_same_v<WeiDataType, float> &&
-                                  is_same_v<OutDataType, ck::bhalf_t>)
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instances(
@@ -765,7 +761,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
 #endif
             }
         }
-        else if constexpr(NumDimSpatial == 3)
+        if constexpr(NumDimSpatial == 3)
         {
             if constexpr(is_same_v<InLayout, GNDHWC> && is_same_v<WeiLayout, GKZYXC> &&
                          is_same_v<OutLayout, GNDHWK>)
@@ -783,8 +779,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_FP16
-                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                                  is_same_v<OutDataType, half_t>)
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_f16_instances(
@@ -799,9 +795,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_BF16
-                else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
-                                  is_same_v<WeiDataType, float> &&
-                                  is_same_v<OutDataType, ck::bhalf_t>)
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
@@ -822,8 +817,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
             }
-            else if constexpr(is_same_v<InLayout, NDHWGC> && is_same_v<WeiLayout, GKZYXC> &&
-                              is_same_v<OutLayout, NDHWGK>)
+            if constexpr(is_same_v<InLayout, NDHWGC> && is_same_v<WeiLayout, GKZYXC> &&
+                         is_same_v<OutLayout, NDHWGK>)
             {
 #ifdef CK_ENABLE_FP32
                 if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
@@ -838,10 +833,9 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_FP16
-                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                                  is_same_v<OutDataType, half_t> &&
-                                  is_same_v<ComputeTypeA, half_t> &&
-                                  is_same_v<ComputeTypeB, half_t>)
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> &&
+                             is_same_v<ComputeTypeB, half_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
@@ -856,9 +850,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #ifdef CK_ENABLE_BF16
-                else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
-                                  is_same_v<WeiDataType, float> &&
-                                  is_same_v<OutDataType, ck::bhalf_t>)
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t>)
                 {
 #ifdef DL_KERNELS
                     add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
@@ -879,9 +872,9 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 }
 #endif
 #if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
-                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
-                                  is_same_v<OutDataType, half_t> &&
-                                  is_same_v<ComputeTypeA, bf8_t> && is_same_v<ComputeTypeB, f8_t>)
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, bf8_t> &&
+                             is_same_v<ComputeTypeB, f8_t>)
                 {
                     add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(
                         op_ptrs);

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

@@ -19,13 +19,13 @@ namespace instance {
 #ifdef CK_ENABLE_FP16
 // FP16
 void add_device_normalization_rank_2_1_f16_instances(
-    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 2, 1>>>&);
+    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, PassThrough, 2, 1>>>&);
 
 void add_device_normalization_rank_4_3_f16_instances(
-    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 4, 3>>>&);
+    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, PassThrough, 4, 3>>>&);
 
 void add_device_normalization_rank_5_3_f16_instances(
-    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, PassThrough, 5, 3>>>&);
+    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, PassThrough, 5, 3>>>&);
 #endif
 #ifdef CK_ENABLE_FP32
 // FP32
@@ -42,14 +42,15 @@ template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           index_t Rank,
           index_t NumReduceDim>
 struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormalization<
     XDataType,
     GammaDataType,
     BetaDataType,
-    F32,
     YDataType,
+    SaveMeanInvStdDataType,
     ck::tensor_operation::element_wise::PassThrough,
     Rank,
     NumReduceDim>>
@@ -57,8 +58,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormal
     using DeviceOp = DeviceNormalization<XDataType,
                                          GammaDataType,
                                          BetaDataType,
-                                         F32,
                                          YDataType,
+                                         SaveMeanInvStdDataType,
                                          ck::tensor_operation::element_wise::PassThrough,
                                          Rank,
                                          NumReduceDim>;
@@ -68,7 +69,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormal
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
 #ifdef CK_ENABLE_FP16
         if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> &&
-                     is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16>)
+                     is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16> &&
+                     is_same_v<SaveMeanInvStdDataType, F32>)
         {
             if constexpr(Rank == 2 && NumReduceDim == 1)
             {
@@ -86,7 +88,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormal
 #endif
 #ifdef CK_ENABLE_FP32
         if constexpr(is_same_v<XDataType, F32> && is_same_v<GammaDataType, F32> &&
-                     is_same_v<BetaDataType, F32> && is_same_v<YDataType, F32>)
+                     is_same_v<BetaDataType, F32> && is_same_v<YDataType, F32> &&
+                     is_same_v<SaveMeanInvStdDataType, F32>)
         {
             if constexpr(Rank == 2 && NumReduceDim == 1)
             {

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

@@ -19,7 +19,7 @@ namespace instance {
 
 // FP16
 void add_device_normalization_rank_5_3_swish_f16_instances(
-    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Swish, 5, 3>>>&);
+    std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, Swish, 5, 3>>>&);
 
 // FP32
 void add_device_normalization_rank_5_3_swish_f32_instances(
@@ -27,20 +27,21 @@ void add_device_normalization_rank_5_3_swish_f32_instances(
 
 // [x, gamma, beta, y] = [f16, f32, f32, f16]
 void add_device_normalization_rank_5_3_swish_f16_f32_f32_f16_instances(
-    std::vector<std::unique_ptr<DeviceNormalization<F16, F32, F32, F32, F16, Swish, 5, 3>>>&);
+    std::vector<std::unique_ptr<DeviceNormalization<F16, F32, F32, F16, F32, Swish, 5, 3>>>&);
 
 template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
           typename YDataType,
+          typename SaveMeanInvStdDataType,
           index_t Rank,
           index_t NumReduceDim>
 struct DeviceOperationInstanceFactory<
     ck::tensor_operation::device::DeviceNormalization<XDataType,
                                                       GammaDataType,
                                                       BetaDataType,
-                                                      F32,
                                                       YDataType,
+                                                      SaveMeanInvStdDataType,
                                                       ck::tensor_operation::element_wise::Swish,
                                                       Rank,
                                                       NumReduceDim>>
@@ -48,8 +49,8 @@ struct DeviceOperationInstanceFactory<
     using DeviceOp = DeviceNormalization<XDataType,
                                          GammaDataType,
                                          BetaDataType,
-                                         F32,
                                          YDataType,
+                                         SaveMeanInvStdDataType,
                                          ck::tensor_operation::element_wise::Swish,
                                          Rank,
                                          NumReduceDim>;
@@ -59,7 +60,8 @@ struct DeviceOperationInstanceFactory<
         std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
 
         if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> &&
-                     is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16>)
+                     is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16> &&
+                     is_same_v<SaveMeanInvStdDataType, F32>)
         {
             if constexpr(Rank == 5 && NumReduceDim == 3)
             {
@@ -67,7 +69,8 @@ struct DeviceOperationInstanceFactory<
             }
         }
         else if constexpr(is_same_v<XDataType, F32> && is_same_v<GammaDataType, F32> &&
-                          is_same_v<BetaDataType, F32> && is_same_v<YDataType, F32>)
+                          is_same_v<BetaDataType, F32> && is_same_v<YDataType, F32> &&
+                          is_same_v<SaveMeanInvStdDataType, F32>)
         {
             if constexpr(Rank == 5 && NumReduceDim == 3)
             {
@@ -75,7 +78,8 @@ struct DeviceOperationInstanceFactory<
             }
         }
         else if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F32> &&
-                          is_same_v<BetaDataType, F32> && is_same_v<YDataType, F16>)
+                          is_same_v<BetaDataType, F32> && is_same_v<YDataType, F16> &&
+                          is_same_v<SaveMeanInvStdDataType, F32>)
         {
             if constexpr(Rank == 5 && NumReduceDim == 3)
             {

library/include/ck/library/utility/check_err.hpp

@@ -230,7 +230,6 @@ check_err(const Range& out,
     return res;
 }
 
-#if defined CK_ENABLE_FP8
 template <typename Range, typename RefRange>
 std::enable_if_t<(std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
                   std::is_same_v<ranges::range_value_t<Range>, f8_t>),
@@ -275,9 +274,7 @@ check_err(const Range& out,
     }
     return res;
 }
-#endif
 
-#if defined CK_ENABLE_BF8
 template <typename Range, typename RefRange>
 std::enable_if_t<(std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
                   std::is_same_v<ranges::range_value_t<Range>, bf8_t>),
@@ -322,7 +319,6 @@ check_err(const Range& out,
     }
     return res;
 }
-#endif
 
 } // namespace utils
 } // namespace ck

library/include/ck/library/utility/host_common_util.hpp

@@ -22,7 +22,7 @@ static inline void dumpBufferToFile(const char* fileName, T* data, size_t dataNu
     std::ofstream outFile(fileName, std::ios::binary);
     if(outFile)
     {
-        outFile.write(reinterpret_cast<char*>(data), dataNumItems * sizeof(T));
+        outFile.write(reinterpret_cast<const char*>(data), dataNumItems * sizeof(T));
         outFile.close();
         std::cout << "Write output to file " << fileName << std::endl;
     }

library/include/ck/library/utility/host_tensor_generator.hpp

@@ -200,10 +200,11 @@ struct GeneratorTensor_3<ck::bf8_t>
 template <typename T>
 struct GeneratorTensor_4
 {
-    std::default_random_engine generator;
+    std::mt19937 generator;
     std::normal_distribution<float> distribution;
 
-    GeneratorTensor_4(float mean, float stddev) : generator(1), distribution(mean, stddev){};
+    GeneratorTensor_4(float mean, float stddev, unsigned int seed = 1)
+        : generator(seed), distribution(mean, stddev){};
 
     template <typename... Is>
     T operator()(Is...)