AIT Attention API refactor (#8)

* sanity pass

* sanity pass 2

* confirm significant performance regression.

* turn on all instances

* turn off instance format

* Fix bug & tunning & format

* DML meta, self_attn+cross_attn

* sanity pass

* remove useless flag

* update tile and problem size used in AIT attention

* bug fix in grouped conv supporting check

example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt

@@ -8,6 +8,8 @@ add_example_executable(example_grouped_gemm_lower_triangle_scale_softmax_gemm_pe
 if(GPU_TARGETS MATCHES "gfx1100" OR GPU_TARGETS MATCHES "gfx1101" OR GPU_TARGETS MATCHES "gfx1102")
     add_example_executable(example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16.cpp)
     add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_wmma_fp16 batched_gemm_scale_softmax_gemm_permute_wmma_fp16.cpp)
+    add_example_executable(example_self_attention_forward_wmma_fp16 self_attention_forward_wmma_fp16.cpp)
+    add_example_executable(example_cross_attention_forward_wmma_fp16 cross_attention_forward_wmma_fp16.cpp)
 endif()
 
 add_custom_target(example_gemm_scale_softmax_gemm)

example/32_batched_gemm_scale_softmax_gemm/batched_gemm_scale_softmax_gemm_permute_wmma_fp16.cpp

@@ -67,77 +67,200 @@ static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecial
 static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
 static constexpr auto TensorSpecC  = ck::tensor_operation::device::TensorSpecialization::Default;
 
-using DeviceGemmInstance =
-    ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
-        NumDimG,
-        NumDimM,
-        NumDimN,
-        NumDimK,
-        NumDimO,
-        ADataType,
-        B0DataType,
-        B1DataType,
-        CDataType,
-        Acc0BiasDataType,
-        Acc0DataType,
-        Acc1BiasDataType,
-        Acc1DataType,
-        CShuffleDataType,
-        AElementOp,
-        B0ElementOp,
-        Acc0ElementOp,
-        B1ElementOp,
-        CElementOp,
-        GemmSpec,
-        TensorSpecA,
-        TensorSpecB0,
-        TensorSpecB1,
-        TensorSpecC,
-        1,
-        256,
-        //      Gemm 0
-        128, // MPerBlock
-        64,  // LPerBlock
-        64,  // KPerBlock
-        8,   // K1
-        //      Gemm 1
-        64, // NPerBlock
-        64, // LTilePerBlock
-        8,  // L1
-        16, // MPerWMMA
-        16, // LPerWMMA
-        16, // NPerWMMA
-        // Per repeat = wave_m = wave_num, wave_n = 1
-        1,           // MRepeat
-        4,           // LRepeat
-        4,           // NRepeat
-        S<4, 64, 1>, // ABlockTransfer MK -> K0 M K1
-        S<1, 0, 2>,
-        S<1, 0, 2>,
-        2,
-        8,
-        8,
-        true,
-        S<4, 64, 1>, // B0BlockTransfer LK -> K0 L K1
-        S<1, 0, 2>,
-        S<1, 0, 2>,
-        2,
-        8,
-        8,
-        true,
-        S<4, 8, 8>, // B1BlockTransfer NL -> L0 N L1
-        S<0, 2, 1>,
-        S<0, 2, 1>,
-        1,
-        8,
-        1,
-        false,
-        1,              // CShuffleMWmmaPerWavePerShuffle
-        2,              // CShuffleNWmmaPerWavePerShuffle
-        S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
-        8,              // CShuffleBlockTransferScalarPerVector_NPerBlock
-        MaskingSpec>;   // MaskingSpecialization
-
+// clang-format off
+// #define CK_MHA_USE_WAVE_1
+// #define CK_MHA_USE_WAVE_2
+#define CK_MHA_USE_WAVE_4
+#define CK_MHA_USE_WAVE_8
+using DeviceMHAFactory = 
+    std::tuple<
+#ifdef CK_MHA_USE_WAVE_1
+        // 1 wave, mrepeat = 1, nrepeat = 2, k/o repeat = 1~5
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            32,
+            //      Gemm 0
+            16, 128, 64, 8,  8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 16, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            32,
+            //      Gemm 0
+            16, 64, 64, 8,  8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 16, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_2
+         ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            64,
+            //      Gemm 0
+            32, 128, 64, 8, 8,
+            //      Gemm 1
+                 64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 32, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            64,
+            //      Gemm 0
+            32, 64, 64, 8, 8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 32, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_4
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            128,
+            //      Gemm 0
+            64, 128, 64, 8, 8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 64, 1, 2>, 8,
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            128,
+            //      Gemm 0
+            64, 64, 64, 8, 8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 64, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_8
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            256,
+            //      Gemm 0
+            128, 128, 64, 8, 8,   
+            //      Gemm 1
+                  64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8,  32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2,  16, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 128, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            256,
+            //      Gemm 0
+            128, 128, 64, 8, 8,   
+            //      Gemm 1
+                  64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8,  32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2,  16, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 128, 1, 2>, 8,             
+            MaskingSpec>
+#endif
+    >;
+// clang-format on
 // Ref Gemm0: fp16 in, fp32 out
 using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
                                                                                 B0DataType,

example/32_batched_gemm_scale_softmax_gemm/cross_attention_forward_wmma_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+/*
+Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g_k_l) * B1_g_l_n
+                                                                  |-----------------|
+                                                                          Gemm0
+                                                          |-------------------------------------|
+                                                                          Gemm1
+*/
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_wmma_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType        = F16;
+using B0DataType       = F16;
+using B1DataType       = F16;
+using Acc0DataType     = F32;
+using Acc1DataType     = F32;
+using CShuffleDataType = F32;
+using CDataType        = F16;
+using Acc0BiasDataType = ck::Tuple<>;
+using Acc1BiasDataType = ck::Tuple<>;
+
+static constexpr ck::index_t NumDimG = 2;
+static constexpr ck::index_t NumDimM = 1;
+static constexpr ck::index_t NumDimN = 1;
+static constexpr ck::index_t NumDimK = 1;
+static constexpr ck::index_t NumDimO = 1;
+
+using AElementOp    = PassThrough;
+using B0ElementOp   = PassThrough;
+using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
+using B1ElementOp   = PassThrough;
+using CElementOp    = PassThrough;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
+static constexpr auto MaskingSpec =
+    ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
+
+static constexpr auto TensorSpecA  = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecC  = ck::tensor_operation::device::TensorSpecialization::Default;
+
+// clang-format off
+#define CK_MHA_USE_WAVE_1
+#define CK_MHA_USE_WAVE_2
+#define CK_MHA_USE_WAVE_4
+#define CK_MHA_USE_WAVE_8
+using DeviceMHAFactory = 
+    std::tuple<
+#ifdef CK_MHA_USE_WAVE_1
+        // 1 wave, mrepeat = 1, nrepeat = 2, k/o repeat = 1~5
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            32,
+            //      Gemm 0
+            16, 32, 160, 8, 8, 
+            //      Gemm 1
+                80, 32, 8,
+            16, 16, 16,
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 2, 5,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 16, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            32,
+            //      Gemm 0
+            16, 64, 80, 8, 8, 
+            //      Gemm 1
+                80, 64, 8,
+            16, 16, 16,
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 5,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 16, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            32,
+            //      Gemm 0
+            16, 64, 48, 8,  8,
+            //      Gemm 1
+                48, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 3,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 16, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_2
+         ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            64,
+            //      Gemm 0
+            32, 64, 48, 8,  8,
+            //      Gemm 1
+                48, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 3,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 32, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            64,
+            //      Gemm 0
+            32, 64, 80, 8,  8,
+            //      Gemm 1
+                80, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 5,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 32, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            64,
+            //      Gemm 0
+            32, 32, 160, 8, 8,
+            //      Gemm 1
+                80, 32, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 2, 5,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 32, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_4
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            128,
+            //      Gemm 0
+            64, 128, 80, 8, 8,  
+            //      Gemm 1
+                80, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 5,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 64, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            128,
+            //      Gemm 0
+            64, 192, 48, 8, 8,
+            //      Gemm 1
+                48, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 12, 3,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 64, 1, 2>, 8,
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            128,
+            //      Gemm 0
+            64, 64, 48, 8, 8,
+            //      Gemm 1
+                48, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 3,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 64, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_8
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            256,
+            //      Gemm 0
+            128, 192, 48, 8,4,   
+            //      Gemm 1
+                 48, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 12, 3,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 16, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 128, 1, 2>, 8,             
+            MaskingSpec>
+#endif
+    >;
+// clang-format on
+// Ref Gemm0: fp16 in, fp32 out
+using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
+                                                                                B0DataType,
+                                                                                Acc0DataType,
+                                                                                Acc1DataType,
+                                                                                AElementOp,
+                                                                                B0ElementOp,
+                                                                                Acc0ElementOp>;
+
+// Ref Softmax: fp32 in, fp16 out
+using ReferenceSoftmaxInstance =
+    ck::tensor_operation::host::ReferenceSoftmax<Acc0DataType, ADataType, Acc0DataType>;
+
+// Ref Gemm1: fp16 in, fp16 out
+using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
+                                                                                B1DataType,
+                                                                                CDataType,
+                                                                                Acc1DataType,
+                                                                                AElementOp,
+                                                                                B1ElementOp,
+                                                                                CElementOp>;
+
+#include "run_cross_attention.inc"
+
+int main(int argc, char* argv[]) { return run(argc, argv); }

example/32_batched_gemm_scale_softmax_gemm/run_batched_gemm_scale_softmax_gemm_permute.inc

@@ -127,16 +127,31 @@ int run(int argc, char* argv[])
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
         break;
-    case 6: // Rand: a b0 ; unit: b1	 pass
+    case 6: // Rand: a b0 ; unit: B1
         a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
         break;
-    case 7: // Rand: a b1 ; unit: b0    pass
+    case 7: // Rand: a b1 ; unit: b0
         a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
         b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
         break;
+    case 8: // Rand: a ; unit: b0 b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 9: // Rand: b0 ; unit: a b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 10: // Rand: b1 ; unit: a b0
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
     default:
         a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
         b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
@@ -160,139 +175,166 @@ int run(int argc, char* argv[])
     auto c_element_op    = CElementOp{};
 
     // do GEMM
+    float best_perf         = .0;
+    float best_time         = .0;
+    int not_pass            = 0;
+    std::string best_kernel = "";
+    printf("Verification: %s\n", do_verification ? "ON" : "OFF");
     // TODO ANT: replace array with vector?
-    auto gemm     = DeviceGemmInstance{};
-    auto invoker  = gemm.MakeInvoker();
-    auto argument = gemm.MakeArgument(
-        static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
-        static_cast<B0DataType*>(b0_device_buf.GetDeviceBuffer()),
-        static_cast<B1DataType*>(b1_device_buf.GetDeviceBuffer()),
-        static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
-        {}, // std::array<void*, 1> p_acc0_biases;
-        {}, // std::array<void*, 1> p_acc1_biases;
-        a_gs_ms_ks_lengths,
-        a_gs_ms_ks_strides,
-        b0_gs_ns_ks_lengths,
-        b0_gs_ns_ks_strides,
-        b1_gs_os_ns_lengths,
-        b1_gs_os_ns_strides,
-        c_gs_ms_os_lengths,
-        c_gs_ms_os_strides,
-        {}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
-        {}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
-        {}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
-        {}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_strides},
-        a_element_op,
-        b0_element_op,
-        acc0_element_op,
-        b1_element_op,
-        c_element_op);
-
-    if(!gemm.IsSupportedArgument(argument))
-    {
-        std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
-
-        return 0;
-    }
+    ck::static_for<0, std::tuple_size_v<DeviceMHAFactory>, 1>{}([&](auto i) -> void {
+        const auto device_conv_mha_instance = std::get<i>(DeviceMHAFactory{});
+
+        using DeviceMHAInstance = ck::remove_cvref_t<decltype(device_conv_mha_instance)>;
+        auto gemm               = DeviceMHAInstance{};
+        auto invoker            = gemm.MakeInvoker();
+        auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
+                                          static_cast<B0DataType*>(b0_device_buf.GetDeviceBuffer()),
+                                          static_cast<B1DataType*>(b1_device_buf.GetDeviceBuffer()),
+                                          static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
+                                          M,
+                                          N,
+                                          K,
+                                          O,
+                                          G0,
+                                          G1,
+                                          alpha,
+                                          input_permute,
+                                          output_permute);
+
+        if(!gemm.IsSupportedArgument(argument))
+        {
+            std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
 
-    ck::index_t BatchCount = G0 * G1;
+            // return 0;
+        }
 
-    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+        ck::index_t BatchCount = G0 * G1;
 
-    std::size_t flop      = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
-    std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N +
-                             sizeof(B1DataType) * N * O + sizeof(CDataType) * M * O) *
-                            BatchCount;
+        float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
 
-    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+        std::size_t flop      = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
+        std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N +
+                                 sizeof(B1DataType) * N * O + sizeof(CDataType) * M * O) *
+                                BatchCount;
 
-    float gb_per_sec = num_btype / 1.E6 / ave_time;
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
 
-    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
-              << gemm.GetTypeString() << std::endl;
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
 
-    if(do_verification)
-    {
-        c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
-
-        Tensor<ADataType> a_g_m_k({BatchCount, M, K});
-        Tensor<B0DataType> b0_g_k_n({BatchCount, K, N});
-        Tensor<B1DataType> b1_g_n_o({BatchCount, N, O});
-        Tensor<Acc0DataType> acc0_g_m_n({BatchCount, M, N});       // scratch object after gemm0
-        Tensor<ADataType> a1_g_m_n({BatchCount, M, N});            // scratch object after softmax
-        Tensor<CDataType> c_g_m_o_host_result({BatchCount, M, O}); // scratch object after gemm1
-
-        // permute
-        a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
-            a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
-        });
-        b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
-            b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
-        });
-        b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
-            b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
-        });
-
-        // gemm 0
-        auto ref_gemm0          = ReferenceGemm0Instance{};
-        auto ref_gemm0_invoker  = ref_gemm0.MakeInvoker();
-        auto ref_gemm0_argument = ref_gemm0.MakeArgument(
-            a_g_m_k, b0_g_k_n, acc0_g_m_n, a_element_op, b0_element_op, acc0_element_op);
-
-        ref_gemm0_invoker.Run(ref_gemm0_argument);
-
-        // masking
-        const auto mask = DeviceGemmInstance::C0MatrixMask(N);
-        acc0_g_m_n.ForEach([&](auto& self, auto idx) {
-            if(mask.IsMaskedElement(idx[1], idx[2]))
-                self(idx) = -ck::NumericLimits<float>::Infinity();
-        });
-
-        // softmax
-        auto ref_softmax          = ReferenceSoftmaxInstance{};
-        auto ref_softmax_invoker  = ref_softmax.MakeInvoker();
-        auto ref_softmax_argument = ref_softmax.MakeArgument(acc0_g_m_n, a1_g_m_n, 1, 0, {2});
-
-        ref_softmax_invoker.Run(ref_softmax_argument);
-
-        // gemm1
-        auto ref_gemm1          = ReferenceGemm1Instance{};
-        auto ref_gemm1_invoker  = ref_gemm1.MakeInvoker();
-        auto ref_gemm1_argument = ref_gemm1.MakeArgument(
-            a1_g_m_n, b1_g_n_o, c_g_m_o_host_result, PassThrough{}, b1_element_op, c_element_op);
-
-        ref_gemm1_invoker.Run(ref_gemm1_argument);
-
-        // permute
-        c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
-            const size_t& g0 = idx[0];
-            const size_t& g1 = idx[1];
-
-            const size_t g = g0 * G1 + g1;
-
-            self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
-        });
-
-        // default absolute error and relative error is 0.001
-        double rtol = 1e-3;
-        double atol = 1e-3;
-
-        // when BF16 is taken, set absolute error and relative error to 0.01
-        if(std::is_same_v<ADataType, ck::bhalf_t> && std::is_same_v<B0DataType, ck::bhalf_t> &&
-           std::is_same_v<B1DataType, ck::bhalf_t> && std::is_same_v<CDataType, ck::bhalf_t>)
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+        if(tflops > best_perf)
         {
-            rtol = 1e-2;
-            atol = 1e-2;
+            best_perf   = tflops;
+            best_time   = ave_time * 1000;
+            best_kernel = gemm.GetTypeString();
         }
-
-        return ck::utils::check_err(c_gs_ms_os_device_result.mData,
-                                    c_gs_ms_os_host_result.mData,
-                                    "Error: Incorrect results!",
-                                    rtol,
-                                    atol)
-                   ? 0
-                   : 1;
-    }
-
-    return 0;
+        if(do_verification)
+        {
+            c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
+
+            Tensor<ADataType> a_g_m_k({BatchCount, M, K});
+            Tensor<B0DataType> b0_g_k_n({BatchCount, K, N});
+            Tensor<B1DataType> b1_g_n_o({BatchCount, N, O});
+            Tensor<Acc0DataType> acc0_g_m_n({BatchCount, M, N}); // scratch object after gemm0
+            Tensor<ADataType> a1_g_m_n({BatchCount, M, N});      // scratch object after softmax
+            Tensor<CDataType> c_g_m_o_host_result({BatchCount, M, O}); // scratch object after gemm1
+
+            // permute
+            a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
+                a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+            });
+            b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
+                b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
+            });
+            b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
+                b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
+            });
+
+            // gemm 0
+            auto ref_gemm0          = ReferenceGemm0Instance{};
+            auto ref_gemm0_invoker  = ref_gemm0.MakeInvoker();
+            auto ref_gemm0_argument = ref_gemm0.MakeArgument(
+                a_g_m_k, b0_g_k_n, acc0_g_m_n, a_element_op, b0_element_op, acc0_element_op);
+
+            ref_gemm0_invoker.Run(ref_gemm0_argument);
+
+            // masking
+            const auto mask = typename DeviceMHAInstance::C0MatrixMask(N);
+            acc0_g_m_n.ForEach([&](auto& self, auto idx) {
+                if(mask.IsMaskedElement(idx[1], idx[2]))
+                    self(idx) = -ck::NumericLimits<float>::Infinity();
+            });
+
+            // softmax
+            auto ref_softmax          = ReferenceSoftmaxInstance{};
+            auto ref_softmax_invoker  = ref_softmax.MakeInvoker();
+            auto ref_softmax_argument = ref_softmax.MakeArgument(acc0_g_m_n, a1_g_m_n, 1, 0, {2});
+
+            ref_softmax_invoker.Run(ref_softmax_argument);
+
+            // gemm1
+            auto ref_gemm1          = ReferenceGemm1Instance{};
+            auto ref_gemm1_invoker  = ref_gemm1.MakeInvoker();
+            auto ref_gemm1_argument = ref_gemm1.MakeArgument(a1_g_m_n,
+                                                             b1_g_n_o,
+                                                             c_g_m_o_host_result,
+                                                             PassThrough{},
+                                                             b1_element_op,
+                                                             c_element_op);
+
+            ref_gemm1_invoker.Run(ref_gemm1_argument);
+
+            // permute
+            c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
+                const size_t& g0 = idx[0];
+                const size_t& g1 = idx[1];
+
+                const size_t g = g0 * G1 + g1;
+
+                self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
+            });
+
+            // default absolute error and relative error is 0.001
+            double rtol = 1e-3;
+            double atol = 1e-3;
+
+            // when BF16 is taken, set absolute error and relative error to 0.01
+            if(std::is_same_v<ADataType, ck::bhalf_t> && std::is_same_v<B0DataType, ck::bhalf_t> &&
+               std::is_same_v<B1DataType, ck::bhalf_t> && std::is_same_v<CDataType, ck::bhalf_t>)
+            {
+                rtol = 1e-2;
+                atol = 1e-2;
+            }
+
+            bool this_run_verification = ck::utils::check_err(c_gs_ms_os_device_result.mData,
+                                                              c_gs_ms_os_host_result.mData,
+                                                              "Error: Incorrect results!",
+                                                              rtol,
+                                                              atol);
+            printf("Verification: %s, Pass: %s\n",
+                   do_verification ? "ON" : "OFF",
+                   this_run_verification ? "YES" : "NO");
+
+            if(!this_run_verification)
+            {
+                not_pass = 1;
+                printf("%d th MHA instance verification Failed \n", i.value);
+            }
+        }
+    });
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Problem Size: BatchCount: " << G0 << ", HeadNum: " << G1 << ", M: " << M
+              << ", N: " << N << ", K: " << K << ", O: " << O << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Best kernel: " << best_kernel << " , " << best_perf << " TFlops , " << best_time
+              << " us" << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    return not_pass;
 }

example/32_batched_gemm_scale_softmax_gemm/run_cross_attention.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+int run(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // GEMM shape for A/B0/B1/C
+    // C_g_m_o = A_g_m_k * B0_g_k_n * B1_g_n_o
+    ck::index_t M = 256;
+    ck::index_t N = 64;
+    ck::index_t K = 80;
+    ck::index_t O = 80;
+
+    // Output shape C[G0, M, G1, O]. Batch dim, outer dim, inner dim must match GEMM shape
+    // C_g0_g1_m_o = reshape(C_g_m_o, [g0, g1, m, o])
+    // C_g0_m_g1_o = permute(C_g0_g1_m_o, [0, 2, 1, 3])
+    ck::index_t G0 = 2;
+    ck::index_t G1 = 8;
+
+    float alpha = 1;
+
+    bool input_permute  = false;
+    bool output_permute = true;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 13)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M  = std::stoi(argv[4]);
+        N  = std::stoi(argv[5]);
+        K  = std::stoi(argv[6]);
+        O  = std::stoi(argv[7]);
+        G0 = std::stoi(argv[8]);
+        G1 = std::stoi(argv[9]);
+
+        alpha = std::stof(argv[10]);
+
+        input_permute  = std::stoi(argv[11]);
+        output_permute = std::stoi(argv[12]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 11: M, N, K, O, G0, G1\n");
+        printf("arg10: scale (alpha)\n");
+        printf("arg11 to 12: input / output permute\n");
+        exit(0);
+    }
+
+    std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
+    std::vector<ck::index_t> a_gs_ms_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
+            : std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]
+
+    std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
+    std::vector<ck::index_t> b0_gs_ns_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // B0 layout [G0, N, G1, K]
+            : std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]
+
+    std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
+    std::vector<ck::index_t> b1_gs_os_ns_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // B1 layout [G0, N, G1, O]
+            : std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]
+
+    std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
+    std::vector<ck::index_t> c_gs_ms_os_strides =
+        output_permute
+            ? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
+            : std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]
+
+    Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
+    Tensor<B0DataType> b0_gs_ns_ks(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
+    Tensor<B1DataType> b1_gs_os_ns(b1_gs_os_ns_lengths, b1_gs_os_ns_strides);
+    Tensor<CDataType> c_gs_ms_os_host_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
+    Tensor<CDataType> c_gs_ms_os_device_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
+
+    std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
+    std::cout << "b0_gs_ns_ks: " << b0_gs_ns_ks.mDesc << std::endl;
+    std::cout << "b1_gs_os_ns: " << b1_gs_os_ns.mDesc << std::endl;
+    std::cout << "c_gs_ms_os: " << c_gs_ms_os_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 2:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<B0DataType>{0.0, 1.0});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
+        break;
+    case 3:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
+        break;
+    case 4: // A, B0, B1 1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 5: // Rand: b1 b0; unit: a
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 6: // Rand: a b0 ; unit: B1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 7: // Rand: a b1 ; unit: b0
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 8: // Rand: a ; unit: b0 b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 9: // Rand: b0 ; unit: a b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 10: // Rand: b1 ; unit: a b0
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    default:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
+    }
+
+    std::vector<ck::index_t> kv_gs_ns_ks_lengths{G0, G1, N, 2, K};
+    std::vector<ck::index_t> kv_gs_ns_ks_strides = std::vector<ck::index_t>{
+        N * G1 * 2 * K, 2 * K, G1 * 2 * K, K, 1}; // kv layout [G0, M, G1, 2, K]
+    Tensor<ADataType> kv_gs_ns_ks(kv_gs_ns_ks_lengths, kv_gs_ns_ks_strides);
+    // merge kv into a packed pointer send to device
+    b0_gs_ns_ks.ForEach(
+        [&](auto& self, auto idx) { kv_gs_ns_ks(idx[0], idx[1], idx[2], 0, idx[3]) = self(idx); });
+    b1_gs_os_ns.ForEach(
+        [&](auto& self, auto idx) { kv_gs_ns_ks(idx[0], idx[1], idx[3], 1, idx[2]) = self(idx); });
+    DeviceMem q_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
+    DeviceMem kv_device_buf(sizeof(B0DataType) * b0_gs_ns_ks.mDesc.GetElementSpaceSize() +
+                            sizeof(B1DataType) * b1_gs_os_ns.mDesc.GetElementSpaceSize());
+    DeviceMem c_device_buf(sizeof(CDataType) *
+                           c_gs_ms_os_device_result.mDesc.GetElementSpaceSize());
+    q_device_buf.ToDevice(a_gs_ms_ks.mData.data());
+    kv_device_buf.ToDevice(kv_gs_ns_ks.mData.data());
+
+    auto a_element_op    = AElementOp{};
+    auto b0_element_op   = B0ElementOp{};
+    auto acc0_element_op = Acc0ElementOp{alpha};
+    auto b1_element_op   = B1ElementOp{};
+    auto c_element_op    = CElementOp{};
+
+    // do GEMM
+    float best_perf         = .0;
+    float best_time         = .0;
+    int not_pass            = 0;
+    std::string best_kernel = "";
+    printf("Verification: %s\n", do_verification ? "ON" : "OFF");
+    // TODO ANT: replace array with vector?
+    ck::static_for<0, std::tuple_size_v<DeviceMHAFactory>, 1>{}([&](auto i) -> void {
+        const auto device_conv_mha_instance = std::get<i>(DeviceMHAFactory{});
+
+        using DeviceMHAInstance = ck::remove_cvref_t<decltype(device_conv_mha_instance)>;
+        auto gemm               = DeviceMHAInstance{};
+        auto invoker            = gemm.MakeCrossAttnInvoker();
+        auto argument =
+            gemm.MakeCrossAttnArgument(static_cast<ADataType*>(q_device_buf.GetDeviceBuffer()),
+                                       static_cast<B0DataType*>(kv_device_buf.GetDeviceBuffer()),
+                                       static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
+                                       G0,
+                                       M,
+                                       N,
+                                       G1,
+                                       K,
+                                       alpha);
+
+        // if(!gemm.IsSupportedArgument(argument))
+        // {
+        //     std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        //     return 0;
+        // }
+
+        ck::index_t BatchCount = G0 * G1;
+
+        float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+        std::size_t flop      = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
+        std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N +
+                                 sizeof(B1DataType) * N * O + sizeof(CDataType) * M * O) *
+                                BatchCount;
+
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+        if(tflops > best_perf)
+        {
+            best_perf   = tflops;
+            best_time   = ave_time * 1000;
+            best_kernel = gemm.GetTypeString();
+        }
+        if(do_verification)
+        {
+            c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
+
+            Tensor<ADataType> a_g_m_k({BatchCount, M, K});
+            Tensor<B0DataType> b0_g_k_n({BatchCount, K, N});
+            Tensor<B1DataType> b1_g_n_o({BatchCount, N, O});
+            Tensor<Acc0DataType> acc0_g_m_n({BatchCount, M, N}); // scratch object after gemm0
+            Tensor<ADataType> a1_g_m_n({BatchCount, M, N});      // scratch object after softmax
+            Tensor<CDataType> c_g_m_o_host_result({BatchCount, M, O}); // scratch object after gemm1
+
+            // permute
+            a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
+                a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+            });
+            b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
+                b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
+            });
+            b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
+                b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
+            });
+
+            // gemm 0
+            auto ref_gemm0          = ReferenceGemm0Instance{};
+            auto ref_gemm0_invoker  = ref_gemm0.MakeInvoker();
+            auto ref_gemm0_argument = ref_gemm0.MakeArgument(
+                a_g_m_k, b0_g_k_n, acc0_g_m_n, a_element_op, b0_element_op, acc0_element_op);
+
+            ref_gemm0_invoker.Run(ref_gemm0_argument);
+
+            // masking
+            const auto mask = typename DeviceMHAInstance::C0MatrixMask(N);
+            acc0_g_m_n.ForEach([&](auto& self, auto idx) {
+                if(mask.IsMaskedElement(idx[1], idx[2]))
+                    self(idx) = -ck::NumericLimits<float>::Infinity();
+            });
+
+            // softmax
+            auto ref_softmax          = ReferenceSoftmaxInstance{};
+            auto ref_softmax_invoker  = ref_softmax.MakeInvoker();
+            auto ref_softmax_argument = ref_softmax.MakeArgument(acc0_g_m_n, a1_g_m_n, 1, 0, {2});
+
+            ref_softmax_invoker.Run(ref_softmax_argument);
+
+            // gemm1
+            auto ref_gemm1          = ReferenceGemm1Instance{};
+            auto ref_gemm1_invoker  = ref_gemm1.MakeInvoker();
+            auto ref_gemm1_argument = ref_gemm1.MakeArgument(a1_g_m_n,
+                                                             b1_g_n_o,
+                                                             c_g_m_o_host_result,
+                                                             PassThrough{},
+                                                             b1_element_op,
+                                                             c_element_op);
+
+            ref_gemm1_invoker.Run(ref_gemm1_argument);
+
+            // permute
+            c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
+                const size_t& g0 = idx[0];
+                const size_t& g1 = idx[1];
+
+                const size_t g = g0 * G1 + g1;
+
+                self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
+            });
+
+            // default absolute error and relative error is 0.001
+            double rtol = 1e-3;
+            double atol = 1e-3;
+
+            // when BF16 is taken, set absolute error and relative error to 0.01
+            if(std::is_same_v<ADataType, ck::bhalf_t> && std::is_same_v<B0DataType, ck::bhalf_t> &&
+               std::is_same_v<B1DataType, ck::bhalf_t> && std::is_same_v<CDataType, ck::bhalf_t>)
+            {
+                rtol = 1e-2;
+                atol = 1e-2;
+            }
+
+            bool this_run_verification = ck::utils::check_err(c_gs_ms_os_device_result.mData,
+                                                              c_gs_ms_os_host_result.mData,
+                                                              "Error: Incorrect results!",
+                                                              rtol,
+                                                              atol);
+            printf("Verification: %s, Pass: %s\n",
+                   do_verification ? "ON" : "OFF",
+                   this_run_verification ? "YES" : "NO");
+
+            if(!this_run_verification)
+            {
+                not_pass = 1;
+                printf("%d th MHA instance verification Failed \n", i.value);
+            }
+        }
+    });
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Problem Size: BatchCount: " << G0 << ", HeadNum: " << G1 << ", M: " << M
+              << ", N: " << N << ", K: " << K << ", O: " << O << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Best kernel: " << best_kernel << " , " << best_perf << " TFlops , " << best_time
+              << " us" << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    return not_pass;
+}

example/32_batched_gemm_scale_softmax_gemm/run_self_attention.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+int run(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // GEMM shape for A/B0/B1/C
+    // C_g_m_o = A_g_m_k * B0_g_k_n * B1_g_n_o
+    ck::index_t M = 256;
+    ck::index_t N = 256;
+    ck::index_t K = 80;
+    ck::index_t O = 80;
+
+    // Output shape C[G0, M, G1, O]. Batch dim, outer dim, inner dim must match GEMM shape
+    // C_g0_g1_m_o = reshape(C_g_m_o, [g0, g1, m, o])
+    // C_g0_m_g1_o = permute(C_g0_g1_m_o, [0, 2, 1, 3])
+    ck::index_t G0 = 2;
+    ck::index_t G1 = 8;
+
+    float alpha = 1;
+
+    bool input_permute  = false;
+    bool output_permute = true;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 13)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M  = std::stoi(argv[4]);
+        N  = std::stoi(argv[5]);
+        K  = std::stoi(argv[6]);
+        O  = std::stoi(argv[7]);
+        G0 = std::stoi(argv[8]);
+        G1 = std::stoi(argv[9]);
+
+        alpha = std::stof(argv[10]);
+
+        input_permute  = std::stoi(argv[11]);
+        output_permute = std::stoi(argv[12]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 11: M, N, K, O, G0, G1\n");
+        printf("arg10: scale (alpha)\n");
+        printf("arg11 to 12: input / output permute\n");
+        exit(0);
+    }
+
+    std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
+    std::vector<ck::index_t> a_gs_ms_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
+            : std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]
+
+    std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, G1, N, K};
+    std::vector<ck::index_t> b0_gs_ns_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // B0 layout [G0, N, G1, K]
+            : std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]
+
+    std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, G1, O, N};
+    std::vector<ck::index_t> b1_gs_os_ns_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // B1 layout [G0, N, G1, O]
+            : std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]
+
+    std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
+    std::vector<ck::index_t> c_gs_ms_os_strides =
+        output_permute
+            ? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
+            : std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]
+
+    Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
+    Tensor<B0DataType> b0_gs_ns_ks(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
+    Tensor<B1DataType> b1_gs_os_ns(b1_gs_os_ns_lengths, b1_gs_os_ns_strides);
+    Tensor<CDataType> c_gs_ms_os_host_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
+    Tensor<CDataType> c_gs_ms_os_device_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
+
+    std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
+    std::cout << "b0_gs_ns_ks: " << b0_gs_ns_ks.mDesc << std::endl;
+    std::cout << "b1_gs_os_ns: " << b1_gs_os_ns.mDesc << std::endl;
+    std::cout << "c_gs_ms_os: " << c_gs_ms_os_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 2:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<B0DataType>{0.0, 1.0});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
+        break;
+    case 3:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
+        break;
+    case 4: // A, B0, B1 1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 5: // Rand: b1 b0; unit: a
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 6: // Rand: a b0 ; unit: B1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 7: // Rand: a b1 ; unit: b0
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 8: // Rand: a ; unit: b0 b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 9: // Rand: b0 ; unit: a b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 10: // Rand: b1 ; unit: a b0
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    default:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
+    }
+
+    std::vector<ck::index_t> qkv_gs_ms_ks_lengths{G0, G1, M, 3, K};
+    std::vector<ck::index_t> qkv_gs_ms_ks_strides = std::vector<ck::index_t>{
+        M * G1 * 3 * K, 3 * K, G1 * 3 * K, K, 1}; // qkv layout [G0, M, G1, 3, K]
+    Tensor<ADataType> qkv_gs_ms_ks(qkv_gs_ms_ks_lengths, qkv_gs_ms_ks_strides);
+    // merge qkv into a packed pointer send to device
+    a_gs_ms_ks.ForEach(
+        [&](auto& self, auto idx) { qkv_gs_ms_ks(idx[0], idx[1], idx[2], 0, idx[3]) = self(idx); });
+    b0_gs_ns_ks.ForEach(
+        [&](auto& self, auto idx) { qkv_gs_ms_ks(idx[0], idx[1], idx[2], 1, idx[3]) = self(idx); });
+    b1_gs_os_ns.ForEach(
+        [&](auto& self, auto idx) { qkv_gs_ms_ks(idx[0], idx[1], idx[3], 2, idx[2]) = self(idx); });
+    DeviceMem qkv_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize() +
+                             sizeof(B0DataType) * b0_gs_ns_ks.mDesc.GetElementSpaceSize() +
+                             sizeof(B1DataType) * b1_gs_os_ns.mDesc.GetElementSpaceSize());
+    DeviceMem c_device_buf(sizeof(CDataType) *
+                           c_gs_ms_os_device_result.mDesc.GetElementSpaceSize());
+    qkv_device_buf.ToDevice(qkv_gs_ms_ks.mData.data());
+
+    auto a_element_op    = AElementOp{};
+    auto b0_element_op   = B0ElementOp{};
+    auto acc0_element_op = Acc0ElementOp{alpha};
+    auto b1_element_op   = B1ElementOp{};
+    auto c_element_op    = CElementOp{};
+
+    // do GEMM
+    float best_perf         = .0;
+    float best_time         = .0;
+    int not_pass            = 0;
+    std::string best_kernel = "";
+    printf("Verification: %s\n", do_verification ? "ON" : "OFF");
+    // TODO ANT: replace array with vector?
+    ck::static_for<0, std::tuple_size_v<DeviceMHAFactory>, 1>{}([&](auto i) -> void {
+        const auto device_conv_mha_instance = std::get<i>(DeviceMHAFactory{});
+
+        using DeviceMHAInstance = ck::remove_cvref_t<decltype(device_conv_mha_instance)>;
+        auto gemm               = DeviceMHAInstance{};
+        auto invoker            = gemm.MakeSelfAttnInvoker();
+        auto argument =
+            gemm.MakeSelfAttnArgument(static_cast<ADataType*>(qkv_device_buf.GetDeviceBuffer()),
+                                      static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
+                                      G0,
+                                      M,
+                                      G1,
+                                      K,
+                                      alpha);
+
+        // if(!gemm.IsSupportedArgument(argument))
+        // {
+        //     std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        //     return 0;
+        // }
+
+        ck::index_t BatchCount = G0 * G1;
+
+        float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+        std::size_t flop      = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
+        std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(B0DataType) * K * N +
+                                 sizeof(B1DataType) * N * O + sizeof(CDataType) * M * O) *
+                                BatchCount;
+
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+        if(tflops > best_perf)
+        {
+            best_perf   = tflops;
+            best_time   = ave_time * 1000;
+            best_kernel = gemm.GetTypeString();
+        }
+        if(do_verification)
+        {
+            c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
+
+            Tensor<ADataType> a_g_m_k({BatchCount, M, K});
+            Tensor<B0DataType> b0_g_k_n({BatchCount, K, N});
+            Tensor<B1DataType> b1_g_n_o({BatchCount, N, O});
+            Tensor<Acc0DataType> acc0_g_m_n({BatchCount, M, N}); // scratch object after gemm0
+            Tensor<ADataType> a1_g_m_n({BatchCount, M, N});      // scratch object after softmax
+            Tensor<CDataType> c_g_m_o_host_result({BatchCount, M, O}); // scratch object after gemm1
+
+            // permute
+            a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
+                a_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+            });
+            b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
+                b0_g_k_n(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
+            });
+            b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
+                b1_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
+            });
+
+            // gemm 0
+            auto ref_gemm0          = ReferenceGemm0Instance{};
+            auto ref_gemm0_invoker  = ref_gemm0.MakeInvoker();
+            auto ref_gemm0_argument = ref_gemm0.MakeArgument(
+                a_g_m_k, b0_g_k_n, acc0_g_m_n, a_element_op, b0_element_op, acc0_element_op);
+
+            ref_gemm0_invoker.Run(ref_gemm0_argument);
+
+            // masking
+            const auto mask = typename DeviceMHAInstance::C0MatrixMask(N);
+            acc0_g_m_n.ForEach([&](auto& self, auto idx) {
+                if(mask.IsMaskedElement(idx[1], idx[2]))
+                    self(idx) = -ck::NumericLimits<float>::Infinity();
+            });
+
+            // softmax
+            auto ref_softmax          = ReferenceSoftmaxInstance{};
+            auto ref_softmax_invoker  = ref_softmax.MakeInvoker();
+            auto ref_softmax_argument = ref_softmax.MakeArgument(acc0_g_m_n, a1_g_m_n, 1, 0, {2});
+
+            ref_softmax_invoker.Run(ref_softmax_argument);
+
+            // gemm1
+            auto ref_gemm1          = ReferenceGemm1Instance{};
+            auto ref_gemm1_invoker  = ref_gemm1.MakeInvoker();
+            auto ref_gemm1_argument = ref_gemm1.MakeArgument(a1_g_m_n,
+                                                             b1_g_n_o,
+                                                             c_g_m_o_host_result,
+                                                             PassThrough{},
+                                                             b1_element_op,
+                                                             c_element_op);
+
+            ref_gemm1_invoker.Run(ref_gemm1_argument);
+
+            // permute
+            c_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
+                const size_t& g0 = idx[0];
+                const size_t& g1 = idx[1];
+
+                const size_t g = g0 * G1 + g1;
+
+                self(idx) = c_g_m_o_host_result(g, idx[2], idx[3]);
+            });
+
+            // default absolute error and relative error is 0.001
+            double rtol = 1e-3;
+            double atol = 1e-3;
+
+            // when BF16 is taken, set absolute error and relative error to 0.01
+            if(std::is_same_v<ADataType, ck::bhalf_t> && std::is_same_v<B0DataType, ck::bhalf_t> &&
+               std::is_same_v<B1DataType, ck::bhalf_t> && std::is_same_v<CDataType, ck::bhalf_t>)
+            {
+                rtol = 1e-2;
+                atol = 1e-2;
+            }
+
+            bool this_run_verification = ck::utils::check_err(c_gs_ms_os_device_result.mData,
+                                                              c_gs_ms_os_host_result.mData,
+                                                              "Error: Incorrect results!",
+                                                              rtol,
+                                                              atol);
+            printf("Verification: %s, Pass: %s\n",
+                   do_verification ? "ON" : "OFF",
+                   this_run_verification ? "YES" : "NO");
+
+            if(!this_run_verification)
+            {
+                not_pass = 1;
+                printf("%d th MHA instance verification Failed \n", i.value);
+            }
+        }
+    });
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Problem Size: BatchCount: " << G0 << ", HeadNum: " << G1 << ", M: " << M
+              << ", N: " << N << ", K: " << K << ", O: " << O << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Best kernel: " << best_kernel << " , " << best_perf << " TFlops , " << best_time
+              << " us" << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    return not_pass;
+}

example/32_batched_gemm_scale_softmax_gemm/self_attention_forward_wmma_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+/*
+Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g_k_l) * B1_g_l_n
+                                                                  |-----------------|
+                                                                          Gemm0
+                                                          |-------------------------------------|
+                                                                          Gemm1
+*/
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_wmma_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType        = F16;
+using B0DataType       = F16;
+using B1DataType       = F16;
+using Acc0DataType     = F32;
+using Acc1DataType     = F32;
+using CShuffleDataType = F32;
+using CDataType        = F16;
+using Acc0BiasDataType = ck::Tuple<>;
+using Acc1BiasDataType = ck::Tuple<>;
+
+static constexpr ck::index_t NumDimG = 2;
+static constexpr ck::index_t NumDimM = 1;
+static constexpr ck::index_t NumDimN = 1;
+static constexpr ck::index_t NumDimK = 1;
+static constexpr ck::index_t NumDimO = 1;
+
+using AElementOp    = PassThrough;
+using B0ElementOp   = PassThrough;
+using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
+using B1ElementOp   = PassThrough;
+using CElementOp    = PassThrough;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
+static constexpr auto MaskingSpec =
+    ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
+
+static constexpr auto TensorSpecA  = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecC  = ck::tensor_operation::device::TensorSpecialization::Default;
+
+// clang-format off
+#define CK_MHA_USE_WAVE_1
+#define CK_MHA_USE_WAVE_2
+#define CK_MHA_USE_WAVE_4
+#define CK_MHA_USE_WAVE_8
+using DeviceMHAFactory = 
+    std::tuple<
+#ifdef CK_MHA_USE_WAVE_1
+        // 1 wave, mrepeat = 1, nrepeat = 2, k/o repeat = 1~5
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            32,
+            //      Gemm 0
+            16, 128, 64, 8,  8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 16, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            32,
+            //      Gemm 0
+            16, 64, 64, 8,  8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 16, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_2
+         ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            64,
+            //      Gemm 0
+            32, 128, 64, 8, 8,
+            //      Gemm 1
+                 64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 32, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            64,
+            //      Gemm 0
+            32, 64, 64, 8, 8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 32, 1, 2>, 8,             
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_4
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            128,
+            //      Gemm 0
+            64, 128, 64, 8, 8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 64, 1, 2>, 8,
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            128,
+            //      Gemm 0
+            64, 64, 64, 8, 8,
+            //      Gemm 1
+                64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 4, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 64, 1, 2>, 8,           
+            MaskingSpec>,
+#endif
+#ifdef CK_MHA_USE_WAVE_8
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            256,
+            //      Gemm 0
+            128, 128, 64, 8, 8,   
+            //      Gemm 1
+                  64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8,  32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2,  16, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 128, 1, 2>, 8,             
+            MaskingSpec>,
+        ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
+            NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
+            ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
+            AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
+            GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
+            256,
+            //      Gemm 0
+            128, 128, 64, 8, 8,   
+            //      Gemm 1
+                  64, 64, 8,  
+            16, 16, 16, 
+            // Per repeat = wave_m = wave_num, wave_n = 1
+            1, 8, 4,
+            // ABlockTransfer MK -> K0 M K1
+            S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B0BlockTransfer LK -> K0 L K1
+            S<8,  32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
+            // B1BlockTransfer NL -> L0 N L1
+            S<2,  16, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, false,
+            // CShuffleBlockTransfer MN
+            1, 1, S<1, 128, 1, 2>, 8,             
+            MaskingSpec>
+#endif
+    >;
+// clang-format on
+// Ref Gemm0: fp16 in, fp32 out
+using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
+                                                                                B0DataType,
+                                                                                Acc0DataType,
+                                                                                Acc1DataType,
+                                                                                AElementOp,
+                                                                                B0ElementOp,
+                                                                                Acc0ElementOp>;
+
+// Ref Softmax: fp32 in, fp16 out
+using ReferenceSoftmaxInstance =
+    ck::tensor_operation::host::ReferenceSoftmax<Acc0DataType, ADataType, Acc0DataType>;
+
+// Ref Gemm1: fp16 in, fp16 out
+using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
+                                                                                B1DataType,
+                                                                                CDataType,
+                                                                                Acc1DataType,
+                                                                                AElementOp,
+                                                                                B1ElementOp,
+                                                                                CElementOp>;
+
+#include "run_self_attention.inc"
+
+int main(int argc, char* argv[]) { return run(argc, argv); }

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

@@ -650,7 +650,7 @@ struct DeviceGroupedConvFwdMultipleD_Wmma_CShuffle
             // check if it's 1x1, stride=1 conv
             for(index_t i = 0; i < NDimSpatial; ++i)
             {
-                const index_t X          = arg.b_g_k_c_xs_lengths_[i + 2];
+                const index_t X          = arg.b_g_k_c_xs_lengths_[i + 3];
                 const index_t ConvStride = arg.conv_filter_strides_[i];
                 const index_t LeftPad    = arg.input_left_pads_[i];
                 const index_t RightPad   = arg.input_right_pads_[i];
@@ -667,7 +667,7 @@ struct DeviceGroupedConvFwdMultipleD_Wmma_CShuffle
             // check if it's 1x1 conv
             for(index_t i = 0; i < NDimSpatial; ++i)
             {
-                const index_t X        = arg.b_g_k_c_xs_lengths_[i + 2];
+                const index_t X        = arg.b_g_k_c_xs_lengths_[i + 3];
                 const index_t LeftPad  = arg.input_left_pads_[i];
                 const index_t RightPad = arg.input_right_pads_[i];
 

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

@@ -53,7 +53,10 @@ struct MaskOutUpperTrianglePredicate
 template <typename MaskOutPredicate>
 struct C0MatrixMask_impl
 {
-    C0MatrixMask_impl(index_t NRaw) : NRaw_(NRaw), predicate_(MaskOutPredicate{}) {}
+    __host__ __device__ C0MatrixMask_impl(index_t NRaw)
+        : NRaw_(NRaw), predicate_(MaskOutPredicate{})
+    {
+    }
 
     __host__ __device__ constexpr bool IsNOutOfBound(/*index_t m, */ index_t n) const
     {

include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_wmma_cshuffle.hpp

@@ -18,102 +18,6 @@
 
 namespace ck {
 
-template <typename GridwiseOp,
-          typename ADataType,
-          typename B0DataType,
-          typename B1DataType,
-          typename CDataType,
-          typename AGridDesc,
-          typename B0GridDesc,
-          typename B1GridDesc,
-          typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename AElementwiseOperation,
-          typename B0ElementwiseOperation,
-          typename AccElementwiseOperation,
-          typename B1ElementwiseOperation,
-          typename CElementwiseOperation,
-          typename ComputeBasePtrOfStridedBatch,
-          typename C0MatrixMask,
-          typename Block2CTileMap,
-          bool HasMainKBlockLoop>
-__global__ void
-#if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
-#endif
-        kernel_batched_gemm_softmax_gemm_wmma_cshuffle(
-            const ADataType* __restrict__ p_a_grid,
-            const B0DataType* __restrict__ p_b0_grid,
-            const B1DataType* __restrict__ p_b1_grid,
-            CDataType* __restrict__ p_c_grid,
-            const AGridDesc a_grid_desc,
-            const B0GridDesc b0_grid_desc,
-            const B1GridDesc b1_grid_desc,
-            const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
-                c_grid_desc_mblock_mperblock_nblock_nperblock,
-            const AElementwiseOperation a_element_op,
-            const B0ElementwiseOperation b0_element_op,
-            const AccElementwiseOperation acc_element_op,
-            const B1ElementwiseOperation b1_element_op,
-            const CElementwiseOperation c_element_op,
-            const index_t batch_count,
-            const ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch,
-            const C0MatrixMask c0_matrix_mask,
-            const Block2CTileMap block_2_ctile_map)
-{
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
-    defined(__gfx1102__))
-    __shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
-
-    const index_t num_blocks_per_batch =
-        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
-    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
-
-    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
-    const long_index_t b0_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB0BasePtr(g_idx)));
-    const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
-    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));
-
-    GridwiseOp::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
-                                                p_b0_grid + b0_batch_offset,
-                                                p_b1_grid + b1_batch_offset,
-                                                p_c_grid + c_batch_offset,
-                                                p_shared,
-                                                a_grid_desc,
-                                                b0_grid_desc,
-                                                b1_grid_desc,
-                                                c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                a_element_op,
-                                                b0_element_op,
-                                                acc_element_op,
-                                                b1_element_op,
-                                                c_element_op,
-                                                c0_matrix_mask,
-                                                block_2_ctile_map);
-#else
-    ignore = p_a_grid;
-    ignore = p_b0_grid;
-    ignore = p_b1_grid;
-    ignore = p_c_grid;
-    ignore = a_grid_desc;
-    ignore = b0_grid_desc;
-    ignore = b1_grid_desc;
-    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = a_element_op;
-    ignore = b0_element_op;
-    ignore = acc_element_op;
-    ignore = b1_element_op;
-    ignore = c_element_op;
-    ignore = batch_count;
-    ignore = compute_base_ptr_of_batch;
-    ignore = c0_matrix_mask;
-    ignore = block_2_ctile_map;
-#endif // end of if (defined(__gfx1100__))
-}
-
 // Gemm0: A [M x K] x B0 [K x L] = Acc [M x L]
 // Gemm1: Acc [M x L] x B1 [L x N] = C [M x N]
 template <typename ADataType,
@@ -136,7 +40,8 @@ template <typename ADataType,
           index_t MPerBlock,
           index_t LPerBlock,
           index_t KPerBlock,
-          index_t K1Value,
+          index_t AK1Value,
+          index_t BK1Value,
           index_t NPerBlock,
           index_t LTilePerBlock,
           index_t L1Value,
@@ -194,9 +99,9 @@ struct GridwiseBatchedGemmSoftmaxGemm_Wmma
     static constexpr auto I6 = Number<6>{};
     static constexpr auto I7 = Number<7>{};
 
-    static constexpr auto AK1 = Number<K1Value>{};
-    static constexpr auto BK0 = Number<KPerBlock / K1Value>{};
-    static constexpr auto BK1 = Number<K1Value>{};
+    static constexpr auto AK1 = Number<AK1Value>{};
+    static constexpr auto BK0 = Number<KPerBlock / BK1Value>{};
+    static constexpr auto BK1 = Number<BK1Value>{};
 
     static constexpr auto L0PerBlock = LTilePerBlock / L1Value;
     static constexpr auto AL0        = Number<L0PerBlock / 2>{};
@@ -714,7 +619,7 @@ struct GridwiseBatchedGemmSoftmaxGemm_Wmma
 
     __host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
     {
-        const index_t num_loop = K / KPerBlock;
+        const index_t num_loop = math::integer_divide_ceil(K, KPerBlock);
 
         return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
     }
@@ -887,7 +792,7 @@ struct GridwiseBatchedGemmSoftmaxGemm_Wmma
                 // Thread-wise copy
                 // KPerBlock/WmmaK -> MRepeat -> MWaves -> WmmaK/K1 -> MPerWmma -> K1
                 constexpr auto KWmmaPerBlock = KPerBlock / WmmaK;
-                constexpr auto K0PerWmma     = WmmaK/2/K1Value;
+                constexpr auto K0PerWmma     = WmmaK/2/AK1Value;
                 auto a_block_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ADataType>(
                     a_block_desc.GetElementSpaceSize());
                 
@@ -903,7 +808,7 @@ struct GridwiseBatchedGemmSoftmaxGemm_Wmma
                                                               Number<K0PerWmma>{},
                                                               I1,
                                                               I1,
-                                                              Number<K1Value>{}>,
+                                                              Number<AK1Value>{}>,
                                                      Sequence<0, 1, 2, 3, 4, 5, 6>,
                                                      6,
                                                      ABlockTransferSrcScalarPerVector,
@@ -966,7 +871,7 @@ struct GridwiseBatchedGemmSoftmaxGemm_Wmma
                 // Thread-wise copy
                 // KPerBlock/WmmaK -> LRepeat -> LWaves -> KRow -> LPerWmma -> K1
                 constexpr auto KWmmaPerBlock = KPerBlock / WmmaK;
-                constexpr auto K0PerWmma     = WmmaK/2/K1Value;
+                constexpr auto K0PerWmma     = WmmaK/2/BK1Value;
                 auto b0_block_buf = make_static_buffer<AddressSpaceEnum::Vgpr, B0DataType>(
                     b0_block_desc.GetElementSpaceSize());
                 
@@ -982,7 +887,7 @@ struct GridwiseBatchedGemmSoftmaxGemm_Wmma
                                                               Number<K0PerWmma>{},
                                                               I1,
                                                               I1,
-                                                              Number<K1Value>{}>,
+                                                              Number<AK1Value>{}>,
                                                      Sequence<0, 1, 2, 3, 4, 5, 6>,
                                                      6,
                                                      B0BlockTransferSrcScalarPerVector,
@@ -1009,7 +914,7 @@ struct GridwiseBatchedGemmSoftmaxGemm_Wmma
 
 /*******************************************************************************/
         // Gemm0
-        constexpr auto KPack = math::integer_least_multiple(K1Value, WmmaK);
+        constexpr auto KPack = math::integer_least_multiple(math::integer_least_multiple(AK1Value,BK1Value), WmmaK);
 
         auto blockwise_gemm0 = BlockwiseGemmWMMA<
             BlockSize,

include/ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp

@@ -16,14 +16,15 @@ template <index_t NumDimG,
           index_t NumDimM,
           index_t NumDimN,
           device::TensorSpecialization TensorSpec>
-static auto MakeGridDescriptorPair(const std::vector<index_t>& gs_ms_ns_lengths_vec,
-                                   const std::vector<index_t>& gs_ms_ns_strides_vec)
+__host__ __device__ static auto
+MakeGridDescriptorPair(const std::array<index_t, NumDimG + NumDimM + NumDimN>& gs_ms_ns_lengths_vec,
+                       const std::array<index_t, NumDimG + NumDimM + NumDimN>& gs_ms_ns_strides_vec)
 {
-    if(!(gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
-         gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN))
-    {
-        throw std::runtime_error("wrong! dimension must match input lengths");
-    }
+    // if(!(gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
+    //      gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN))
+    // {
+    //     throw std::runtime_error("wrong! dimension must match input lengths");
+    // }
 
     const auto to_tuple = [&](auto& vec, auto start, auto end) {
         return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
@@ -143,21 +144,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
     //
     // A
     //
-    static auto MakeAGridDescriptorPair(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
-                                        const std::vector<index_t>& a_gs_ms_ks_strides_vec)
+    __host__ __device__ static auto MakeAGridDescriptorPair(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
     {
         return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimK, ASpec>(a_gs_ms_ks_lengths_vec,
                                                                         a_gs_ms_ks_strides_vec);
     }
 
     // TODO: rename to G_MRaw_KRaw
-    static auto MakeAGridDescriptor_G_M_K(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
-                                          const std::vector<index_t>& a_gs_ms_ks_strides_vec)
+    __host__ __device__ static auto MakeAGridDescriptor_G_M_K(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
     {
         return MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).first;
     }
-    static auto MakeAGridDescriptor_M_K(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
-                                        const std::vector<index_t>& a_gs_ms_ks_strides_vec)
+    __host__ __device__ static auto MakeAGridDescriptor_M_K(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
     {
         return matrix_padder.PadADescriptor_M_K(
             MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).second);
@@ -212,21 +216,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
     //
     // B (alias of B0)
     //
-    static auto MakeB0GridDescriptorPair(const std::vector<index_t>& b0_gs_ns_ks_lengths_vec,
-                                         const std::vector<index_t>& b0_gs_ns_ks_strides_vec)
+    __host__ __device__ static auto MakeB0GridDescriptorPair(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
     {
         return MakeGridDescriptorPair<NumDimG, NumDimN, NumDimK, B0Spec>(b0_gs_ns_ks_lengths_vec,
                                                                          b0_gs_ns_ks_strides_vec);
     }
 
     // TODO: rename to G_MRaw_NRaw
-    static auto MakeB0GridDescriptor_G_N_K(const std::vector<index_t>& b0_gs_ns_ks_lengths_vec,
-                                           const std::vector<index_t>& b0_gs_ns_ks_strides_vec)
+    __host__ __device__ static auto MakeB0GridDescriptor_G_N_K(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
     {
         return MakeB0GridDescriptorPair(b0_gs_ns_ks_lengths_vec, b0_gs_ns_ks_strides_vec).first;
     }
-    static auto MakeB0GridDescriptor_N_K(const std::vector<index_t>& b0_gs_ns_ks_lengths_vec,
-                                         const std::vector<index_t>& b0_gs_ns_ks_strides_vec)
+    __host__ __device__ static auto MakeB0GridDescriptor_N_K(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
     {
         // alias of matrix_padder.PadB0Descriptor_N_K
         return matrix_padder.PadBDescriptor_N_K(
@@ -282,21 +289,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
     //
     // B1
     //
-    static auto MakeB1GridDescriptorPair(const std::vector<index_t>& b1_gs_os_ns_lengths_vec,
-                                         const std::vector<index_t>& b1_gs_os_ns_strides_vec)
+    __host__ __device__ static auto MakeB1GridDescriptorPair(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
     {
         return MakeGridDescriptorPair<NumDimG, NumDimO, NumDimN, B1Spec>(b1_gs_os_ns_lengths_vec,
                                                                          b1_gs_os_ns_strides_vec);
     }
 
     // TODO: rename to G_NRaw_KRaw
-    static auto MakeB1GridDescriptor_G_N_K(const std::vector<index_t>& b1_gs_os_ns_lengths_vec,
-                                           const std::vector<index_t>& b1_gs_os_ns_strides_vec)
+    __host__ __device__ static auto MakeB1GridDescriptor_G_N_K(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
     {
         return MakeB1GridDescriptorPair(b1_gs_os_ns_lengths_vec, b1_gs_os_ns_strides_vec).first;
     }
-    static auto MakeB1GridDescriptor_N_K(const std::vector<index_t>& b1_gs_os_ns_lengths_vec,
-                                         const std::vector<index_t>& b1_gs_os_ns_strides_vec)
+    __host__ __device__ static auto MakeB1GridDescriptor_N_K(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
     {
         // alias of matrix_padder.PadB1Descriptor_O_N
         return matrix_padder.PadB1Descriptor_N_K(
@@ -353,21 +363,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
     //
     // C
     //
-    static auto MakeCGridDescriptorPair(const std::vector<index_t>& c_gs_ms_os_lengths_vec,
-                                        const std::vector<index_t>& c_gs_ms_os_strides_vec)
+    __host__ __device__ static auto MakeCGridDescriptorPair(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
     {
         return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimO, CSpec>(c_gs_ms_os_lengths_vec,
                                                                         c_gs_ms_os_strides_vec);
     }
 
     // TODO: rename to G_MRaw_NRaw
-    static auto MakeCGridDescriptor_G_M_N(const std::vector<index_t>& c_gs_ms_os_lengths_vec,
-                                          const std::vector<index_t>& c_gs_ms_os_strides_vec)
+    __host__ __device__ static auto MakeCGridDescriptor_G_M_N(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
     {
         return MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).first;
     }
-    static auto MakeCGridDescriptor_M_N(const std::vector<index_t>& c_gs_ms_os_lengths_vec,
-                                        const std::vector<index_t>& c_gs_ms_os_strides_vec)
+    __host__ __device__ static auto MakeCGridDescriptor_M_N(
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec,
+        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
     {
         return matrix_padder.PadCDescriptor_M_N(
             MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).second);

script/unet_mha.sh

+#!/bin/bash
+while getopts e: flag
+do
+    case "${flag}" in
+        e) executable=${OPTARG};;
+    esac
+done
+echo "CK-NAVI31 Performance Test: MHA for AITemplate"
+
+VERIFICATION=0
+INITIALIZE=1
+TIMING=1
+
+ALL_TEST_CASE=0
+SELF_ATTENTION=1
+CROSS_ATTENTION=0
+CAUSAL_MASK=0
+# self attention with causal mask
+if  [ $ALL_TEST_CASE -eq 1 ] || { [ $SELF_ATTENTION -eq 1 ] && [ $CAUSAL_MASK -eq 1 ]; }; then
+    echo "Test launched: self attention with causal mask"
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING 4096 4096  40  40 2 8 0.158113881945610 1 1
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING 1024 1024  80  80 2 8 0.111803397536277 1 1
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING  256  256 160 160 2 8 0.079056940972805 1 1
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING   64   64 160 160 2 8 0.079056940972805 1 1
+fi
+
+# cross attention with causal mask
+if [ $ALL_TEST_CASE -eq 1 ] || { [ $CROSS_ATTENTION -eq 1 ] && [ $CAUSAL_MASK -eq 1 ]; }; then
+    echo "Test launched: cross attention with causal mask"
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING 4096   64  40  40 2 8 0.158113881945610 1 1
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING 1024   64  80  80 2 8 0.111803397536277 1 1
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING  256   64 160 160 2 8 0.079056940972805 1 1
+    ./bin/example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 $VERIFICATION 1 $TIMING   64   64 160 160 2 8 0.079056940972805 1 1
+fi
+
+# self attention without causal mask
+if [ $ALL_TEST_CASE -eq 1 ] || { [ $SELF_ATTENTION -eq 1 ] && [ $CAUSAL_MASK -eq 0 ]; }; then
+    echo "Test launched: self attention without causal mask"
+    $executable $VERIFICATION $INITIALIZE $TIMING 4096 4096  64  64 2  5 0.125 1 1
+    $executable $VERIFICATION $INITIALIZE $TIMING 1024 1024  64  64 2 10 0.125 1 1
+    $executable $VERIFICATION $INITIALIZE $TIMING  256  256  64  64 2 20 0.125 1 1
+    $executable $VERIFICATION $INITIALIZE $TIMING   64   64  64  64 2 20 0.125 1 1
+fi
+
+# cross attention without causal mask
+if [ $ALL_TEST_CASE -eq 1 ] || { [ $CROSS_ATTENTION -eq 1 ] && [ $CAUSAL_MASK -eq 0 ]; }; then
+    echo "Test launched: cross attention without causal mask"
+    $executable $VERIFICATION 1 $TIMING 4096   64  40  40 2 8 0.158113881945610 1 1
+    $executable $VERIFICATION 1 $TIMING 1024   64  80  80 2 8 0.111803397536277 1 1
+    $executable $VERIFICATION 1 $TIMING  256   64 160 160 2 8 0.079056940972805 1 1
+    $executable $VERIFICATION 1 $TIMING   64   64 160 160 2 8 0.079056940972805 1 1
+fi
\ No newline at end of file