updated dropout for bwd pt4 & pt5

example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt

@@ -14,6 +14,7 @@ add_example_executable(example_batched_multihead_attention_backward_v2 batched_m
 add_example_executable(example_batched_multihead_attention_backward_v3 batched_multihead_attention_backward_v3.cpp)
 add_example_executable(example_grouped_multihead_attention_train grouped_multihead_attention_train.cpp)
 add_example_executable(example_batched_multihead_attention_train batched_multihead_attention_train.cpp)
+add_example_executable(example_batched_multihead_attention_train_v5 batched_multihead_attention_train_v5.cpp)
 
 add_custom_target(example_gemm_scale_softmax_gemm)
 add_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_xdl_fp16)

example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_train_v5.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_o = Softmax(A_g_m_k * B0_g_k_n) * B1_g_n_o
+                                                                  |-----------------|
+                                                                          Gemm0
+                                                          |-------------------------------------|
+                                                                          Gemm1
+
+Backprop for Gemm + Softmax + Gemm fused operation, where forward prop is defined as:
+
+  Y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
+
+Computation graph:
+
+          K^T                   V
+          |                     |
+          |                     |
+    Q --- * ----- Softmax ----- * --> Y
+              S             P
+
+Kernel inputs:
+
+    Q, K, V, Y, dY, per-row softmax stats (LSE)
+
+Kernel outputs:
+
+    dQ, dK, dV
+
+*/
+
+#define PRINT_HOST 0
+#define USING_MASK 0
+#define DIM 128 // DIM should be a multiple of 8.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <fstream>
+
+#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_multihead_attention_backward_xdl_cshuffle_v4.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle_v5.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_forward_xdl_cshuffle_v2.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"
+#include "ck/library/reference_tensor_operation/cpu/reference_dropout.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16   = ck::half_t;
+using BF16  = ck::bhalf_t;
+using F32   = float;
+using U16   = unsigned short;
+using INT32 = int32_t;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+
+using QKVElementOp = PassThrough;
+using YElementOp   = PassThrough;
+
+using InputDataType    = F16;
+using OutputDataType   = F16;
+using GemmDataType     = F16;
+using AccDataType      = F32;
+using ShuffleDataType  = F32;
+using LSEDataType      = F32;
+using ZDataType        = U16; // INT32
+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;
+// When OutputDataType == F32,      bwd CShuffleBlockTransferScalarPerVector_NPerBlock = 4
+// When OutputDataType == F16/BF16, bwd CShuffleBlockTransferScalarPerVector_NPerBlock = 8
+static constexpr ck::index_t CShuffleBlockTransferScalarPerVector_NPerBlock = 8;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
+#if USING_MASK
+static constexpr auto MaskingSpec =
+    ck::tensor_operation::device::MaskingSpecialization::MaskOutUpperTriangle;
+#else
+static constexpr auto MaskingSpec =
+    ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
+#endif
+
+static constexpr auto TensorSpecQ   = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecK   = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecV   = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr auto TensorSpecY   = ck::tensor_operation::device::TensorSpecialization::Default;
+static constexpr bool Deterministic = false;
+
+// DIM should be a multiple of 8.
+// If      DIM <= 32 , ues prototype1 1st template.
+// If 32 < DIM <= 64 , ues prototype1 2nd template.
+// If 64 < DIM <= 128, ues prototype2 2nd template.
+#if(DIM <= 32)
+using DeviceGemmInstanceFWD =
+    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
+        NumDimG,
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        NumDimO,
+        InputDataType,
+        InputDataType,
+        InputDataType,
+        InputDataType,
+        GemmDataType,
+        ZDataType,
+        LSEDataType,
+        Acc0BiasDataType,
+        Acc1BiasDataType,
+        AccDataType,
+        ShuffleDataType,
+        QKVElementOp,
+        QKVElementOp,
+        Scale,
+        QKVElementOp,
+        YElementOp,
+        GemmSpec,
+        TensorSpecQ,
+        TensorSpecK,
+        TensorSpecV,
+        TensorSpecY,
+        1,
+        256,
+        128,         // MPerBlock
+        128,         // NPerBlock
+        32,          // KPerBlock
+        32,          // Gemm1NPerBlock
+        32,          // Gemm1KPerBlock
+        8,           // AK1
+        8,           // BK1
+        2,           // B1K1
+        32,          // MPerXDL
+        32,          // NPerXDL
+        1,           // MXdlPerWave
+        4,           // NXdlPerWave
+        1,           // Gemm1NXdlPerWave
+        S<4, 64, 1>, // ABlockTransfer
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<4, 64, 1>, // BBlockTransfer
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<16, 16, 1>, // B1BlockTransfer
+        S<0, 2, 1>,
+        S<0, 2, 1>,
+        1,
+        2,
+        2,
+        false,
+        1,              // CShuffleMXdlPerWavePerShuffle
+        1,              // CShuffleNXdlPerWavePerShuffle
+        S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+        8,              // CShuffleBlockTransferScalarPerVector_NPerBlock
+        MaskingSpec,    // MaskingSpecialization
+        Deterministic>;
+
+using DeviceGemmInstanceBWD =
+    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1<
+        NumDimG,
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        NumDimO,
+        InputDataType,
+        OutputDataType,
+        GemmDataType,
+        ZDataType,
+        LSEDataType,
+        Acc0BiasDataType,
+        Acc1BiasDataType,
+        AccDataType,
+        ShuffleDataType,
+        QKVElementOp,
+        QKVElementOp,
+        Scale,
+        QKVElementOp,
+        YElementOp,
+        GemmSpec,
+        TensorSpecQ,
+        TensorSpecK,
+        TensorSpecV,
+        TensorSpecY,
+        1,
+        256,
+        128,
+        128,
+        32,
+        32,
+        32,
+        8,
+        8,
+        2,
+        32,
+        32,
+        4,
+        1,
+        1,
+        1,
+        S<4, 64, 1>,
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<4, 64, 1>,
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        1,
+        1,
+        S<1, 64, 1, 4>,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        MaskingSpec,
+        Deterministic>;
+
+#elif(DIM <= 64)
+using DeviceGemmInstanceFWD =
+    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
+        NumDimG,
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        NumDimO,
+        InputDataType,
+        InputDataType,
+        InputDataType,
+        InputDataType,
+        GemmDataType,
+        ZDataType,
+        LSEDataType,
+        Acc0BiasDataType,
+        Acc1BiasDataType,
+        AccDataType,
+        ShuffleDataType,
+        QKVElementOp,
+        QKVElementOp,
+        Scale,
+        QKVElementOp,
+        YElementOp,
+        GemmSpec,
+        TensorSpecQ,
+        TensorSpecK,
+        TensorSpecV,
+        TensorSpecY,
+        1,
+        256,
+        128,         // MPerBlock
+        128,         // NPerBlock
+        32,          // KPerBlock
+        64,          // Gemm1NPerBlock
+        32,          // Gemm1KPerBlock
+        8,           // AK1
+        8,           // BK1
+        2,           // B1K1
+        32,          // MPerXDL
+        32,          // NPerXDL
+        1,           // MXdlPerWave
+        4,           // NXdlPerWave
+        2,           // Gemm1NXdlPerWave
+        S<4, 64, 1>, // ABlockTransfer
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<4, 64, 1>, // BBlockTransfer
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<16, 16, 1>, // B1BlockTransfer
+        S<0, 2, 1>,
+        S<0, 2, 1>,
+        1,
+        4,
+        2,
+        false,
+        1,              // CShuffleMXdlPerWavePerShuffle
+        2,              // CShuffleNXdlPerWavePerShuffle
+        S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+        8,              // CShuffleBlockTransferScalarPerVector_NPerBlock
+        MaskingSpec,    // MaskingSpecialization
+        Deterministic>;
+
+using DeviceGemmInstanceBWD =
+    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1<
+        NumDimG,
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        NumDimO,
+        InputDataType,
+        OutputDataType,
+        GemmDataType,
+        ZDataType,
+        LSEDataType,
+        Acc0BiasDataType,
+        Acc1BiasDataType,
+        AccDataType,
+        ShuffleDataType,
+        QKVElementOp,
+        QKVElementOp,
+        Scale,
+        QKVElementOp,
+        YElementOp,
+        GemmSpec,
+        TensorSpecQ,
+        TensorSpecK,
+        TensorSpecV,
+        TensorSpecY,
+        1,
+        256,
+        64,
+        128,
+        64,
+        64,
+        32,
+        8,
+        8,
+        2,
+        32,
+        32,
+        2,
+        1,
+        2,
+        1,
+        S<4, 64, 1>,
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<4, 64, 1>,
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        1,
+        2,
+        S<1, 32, 1, 8>,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        MaskingSpec,
+        Deterministic>;
+
+// using DeviceGemmInstanceBWD =
+//     ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2<
+//         NumDimG,
+//         NumDimM,
+//         NumDimN,
+//         NumDimK,
+//         NumDimO,
+//         InputDataType,
+//         OutputDataType,
+//         GemmDataType,
+//         ZDataType,
+//         LSEDataType,
+//         Acc0BiasDataType,
+//         Acc1BiasDataType,
+//         AccDataType,
+//         ShuffleDataType,
+//         QKVElementOp,
+//         QKVElementOp,
+//         Scale,
+//         QKVElementOp,
+//         YElementOp,
+//         GemmSpec,
+//         TensorSpecQ,
+//         TensorSpecK,
+//         TensorSpecV,
+//         TensorSpecY,
+//         1,
+//         256,
+//         128,         // MPerBlock
+//         128,         // NPerBlock
+//         64,          // KPerBlock
+//         64,          // Gemm1NPerBlock
+//         64,          // Gemm1KPerBlock
+//         8,           // AK1
+//         8,           // BK1
+//         2,           // B1K1
+//         32,          // MPerXDL
+//         32,          // NPerXDL
+//         1,           // MXdlPerWave
+//         4,           // NXdlPerWave
+//         2,           // Gemm1NXdlPerWave
+//         2,           // Gemm2NXdlPerWave
+//         S<4, 64, 1>, // ABlockTransfer
+//         S<1, 0, 2>,
+//         S<1, 0, 2>,
+//         2,
+//         8,
+//         8,
+//         true,
+//         S<4, 64, 1>, // BBlockTransfer
+//         S<1, 0, 2>,
+//         S<1, 0, 2>,
+//         2,
+//         8,
+//         8,
+//         true,
+//         S<8, 32, 1>, // B1BlockTransfer
+//         S<0, 2, 1>,
+//         S<0, 2, 1>,
+//         1,
+//         2,
+//         2,
+//         false,
+//         1,              // CShuffleMXdlPerWavePerShuffle
+//         2,              // CShuffleNXdlPerWavePerShuffle
+//         S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+//         CShuffleBlockTransferScalarPerVector_NPerBlock,
+//         MaskingSpec,
+//         Deterministic>;
+#elif(DIM <= 128)
+using DeviceGemmInstanceFWD =
+    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
+        NumDimG,
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        NumDimO,
+        InputDataType,
+        InputDataType,
+        InputDataType,
+        InputDataType,
+        GemmDataType,
+        ZDataType,
+        LSEDataType,
+        Acc0BiasDataType,
+        Acc1BiasDataType,
+        AccDataType,
+        ShuffleDataType,
+        QKVElementOp,
+        QKVElementOp,
+        Scale,
+        QKVElementOp,
+        YElementOp,
+        GemmSpec,
+        TensorSpecQ,
+        TensorSpecK,
+        TensorSpecV,
+        TensorSpecY,
+        1,
+        256,
+        128,         // MPerBlock
+        128,         // NPerBlock
+        32,          // KPerBlock
+        128,         // Gemm1NPerBlock
+        32,          // Gemm1KPerBlock
+        8,           // AK1
+        8,           // BK1
+        2,           // B1K1
+        32,          // MPerXDL
+        32,          // NPerXDL
+        1,           // MXdlPerWave
+        4,           // NXdlPerWave
+        4,           // Gemm1NXdlPerWave
+        S<4, 64, 1>, // ABlockTransfer
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<4, 64, 1>, // BBlockTransfer
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<8, 32, 1>, // B1BlockTransfer
+        S<0, 2, 1>,
+        S<0, 2, 1>,
+        1,
+        4,
+        2,
+        false,
+        1,              // CShuffleMXdlPerWavePerShuffle
+        2,              // CShuffleNXdlPerWavePerShuffle
+        S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+        8,              // CShuffleBlockTransferScalarPerVector_NPerBlock
+        MaskingSpec,    // MaskingSpecialization
+        Deterministic>;
+
+using DeviceGemmInstanceBWD =
+    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2<
+        NumDimG,
+        NumDimM,
+        NumDimN,
+        NumDimK,
+        NumDimO,
+        InputDataType,
+        OutputDataType,
+        GemmDataType,
+        ZDataType,
+        LSEDataType,
+        Acc0BiasDataType,
+        Acc1BiasDataType,
+        AccDataType,
+        ShuffleDataType,
+        QKVElementOp,
+        QKVElementOp,
+        Scale,
+        QKVElementOp,
+        YElementOp,
+        GemmSpec,
+        TensorSpecQ,
+        TensorSpecK,
+        TensorSpecV,
+        TensorSpecY,
+        1,
+        256,
+        64,
+        128,
+        128,
+        128,
+        32,
+        8,
+        8,
+        2,
+        32,
+        32,
+        2,
+        1,
+        4,
+        1,
+        S<4, 64, 1>,
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<4, 64, 1>,
+        S<1, 0, 2>,
+        S<1, 0, 2>,
+        2,
+        8,
+        8,
+        true,
+        S<8, 32, 1>,
+        S<0, 2, 1>,
+        S<0, 2, 1>,
+        1,
+        4,
+        2,
+        false,
+        1,
+        4,
+        S<1, 32, 1, 8>,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        MaskingSpec,
+        Deterministic>;
+
+#endif
+
+// Ref Gemm0: S = alpha * Q * K^T
+// fp16 in, fp32 out
+using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<InputDataType,
+                                                                                InputDataType,
+                                                                                AccDataType,
+                                                                                AccDataType,
+                                                                                PassThrough,
+                                                                                PassThrough,
+                                                                                Scale>;
+
+// Ref Softmax: P = Softmax(S)
+// fp32 in, fp16 out
+using ReferenceSoftmaxInstance =
+    ck::tensor_operation::host::ReferenceSoftmax<AccDataType, InputDataType, AccDataType>;
+
+// Ref Gemm1: Y = P * V
+// fp16 in, fp16 out
+using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<InputDataType,
+                                                                                InputDataType,
+                                                                                InputDataType,
+                                                                                AccDataType,
+                                                                                PassThrough,
+                                                                                PassThrough,
+                                                                                PassThrough>;
+
+// Ref Gemm for backward pass
+// fp16 in, fp16 out
+using ReferenceGemm0GradInstance = ck::tensor_operation::host::ReferenceBatchedGemm<InputDataType,
+                                                                                    InputDataType,
+                                                                                    InputDataType,
+                                                                                    AccDataType,
+                                                                                    PassThrough,
+                                                                                    PassThrough,
+                                                                                    Scale>;
+
+using ReferenceGemm1GradInstance = ck::tensor_operation::host::ReferenceBatchedGemm<InputDataType,
+                                                                                    InputDataType,
+                                                                                    OutputDataType,
+                                                                                    AccDataType,
+                                                                                    PassThrough,
+                                                                                    PassThrough,
+                                                                                    Scale>;
+
+// Ref dropout
+using ReferenceDropoutInstance =
+    ck::tensor_operation::host::ReferenceDropout<ZDataType, InputDataType, InputDataType>;
+
+template <typename TensorQ,
+          typename TensorK,
+          typename TensorV,
+          typename TensorS,
+          typename TensorP,
+          typename TensorZ,
+          typename TensorY,
+          typename TensorLSE = TensorP>
+void run_attention_fwd_host(const TensorQ& q_g_m_k,
+                            const TensorK& k_g_n_k,
+                            const TensorV& v_g_n_o,
+                            const float alpha,
+                            TensorS& s_g_m_n,
+                            TensorP& p_g_m_n,
+                            TensorY& y_g_m_o,
+                            TensorLSE& lse_g_m,
+                            TensorP& p_drop_g_m_n,
+                            TensorZ& z_g_m_n,
+                            ZDataType p_dropout_in_16bits,
+                            float rp_dropout)
+{
+    // S = alpha * Q * K^T
+    auto k_g_k_n            = k_g_n_k.Transpose({0, 2, 1});
+    auto ref_gemm0          = ReferenceGemm0Instance{};
+    auto ref_gemm0_invoker  = ref_gemm0.MakeInvoker();
+    auto ref_gemm0_argument = ref_gemm0.MakeArgument(
+        q_g_m_k, k_g_k_n, s_g_m_n, PassThrough{}, PassThrough{}, Scale{alpha});
+
+    ref_gemm0_invoker.Run(ref_gemm0_argument);
+
+    // masking
+    auto N          = s_g_m_n.GetLengths()[2];
+    const auto mask = DeviceGemmInstanceFWD::C0MatrixMask(N);
+    s_g_m_n.ForEach([&](auto& self, auto idx) {
+        if(mask.IsMaskedElement(idx[1], idx[2]))
+            self(idx) = -ck::NumericLimits<float>::Infinity();
+    });
+
+    // P = Softmax(S)
+    auto ref_softmax          = ReferenceSoftmaxInstance{};
+    auto ref_softmax_invoker  = ref_softmax.MakeInvoker();
+    auto ref_softmax_argument = ref_softmax.MakeArgument(s_g_m_n, p_g_m_n, 1, 0, {2}, &lse_g_m);
+
+    ref_softmax_invoker.Run(ref_softmax_argument);
+
+    // P_dropped
+    auto ref_dropout         = ReferenceDropoutInstance{};
+    auto ref_dropout_invoker = ref_dropout.MakeInvoker();
+    auto ref_dropout_argment =
+        ref_dropout.MakeArgument(z_g_m_n, p_g_m_n, p_drop_g_m_n, p_dropout_in_16bits, rp_dropout);
+    ref_dropout_invoker.Run(ref_dropout_argment);
+
+    // Y = P_dropout * V
+    auto ref_gemm1          = ReferenceGemm1Instance{};
+    auto ref_gemm1_invoker  = ref_gemm1.MakeInvoker();
+    auto ref_gemm1_argument = ref_gemm1.MakeArgument(
+        p_drop_g_m_n, v_g_n_o, y_g_m_o, PassThrough{}, PassThrough{}, PassThrough{});
+
+    ref_gemm1_invoker.Run(ref_gemm1_argument);
+}
+
+int run(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 2; // method 1 will have slightly higher error; TODO: to investigate
+    bool time_kernel     = true;
+
+    // Overall QKV matrices shape
+    // y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
+    // y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
+    // y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
+    ck::index_t N  = 500; // 512
+    ck::index_t M  = 500; // 512
+    ck::index_t K  = DIM;
+    ck::index_t O  = DIM;
+    ck::index_t G0 = 4; // 54
+    ck::index_t G1 = 6; // 16
+
+    bool input_permute  = false;
+    bool output_permute = false;
+
+    float p_drop                    = 0.2;
+    const unsigned long long seed   = 1;
+    const unsigned long long offset = 0;
+
+    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]);
+
+        p_drop = 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);
+    }
+
+    float p_dropout               = 1 - p_drop;
+    ZDataType p_dropout_in_16bits = ZDataType(std::floor(p_dropout * 65535.0));
+    float rp_dropout              = 1.0 / p_dropout;
+    float alpha                   = 1.f / std::sqrt(K);
+
+    std::cout << "do_verification: " << do_verification << std::endl;
+    std::cout << "init_method: " << init_method << std::endl;
+    std::cout << "time_kernel: " << time_kernel << std::endl;
+    std::cout << "M: " << M << std::endl;
+    std::cout << "N: " << N << std::endl;
+    std::cout << "K: " << K << std::endl;
+    std::cout << "O: " << O << std::endl;
+    std::cout << "G0: " << G0 << std::endl;
+    std::cout << "G1: " << G1 << std::endl;
+    std::cout << "alpha: " << alpha << std::endl;
+    std::cout << "input_permute: " << input_permute << std::endl;
+    std::cout << "output_permute: " << output_permute << std::endl;
+    std::cout << "p_drop: " << p_drop << std::endl;
+    std::cout << "seed: " << seed << std::endl;
+    std::cout << "offset: " << offset << std::endl;
+
+    const ck::index_t BatchCount = G0 * G1;
+
+    std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
+    std::vector<ck::index_t> q_gs_ms_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
+            : std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
+
+    std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
+    std::vector<ck::index_t> k_gs_ns_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
+            : std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
+
+    std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
+    std::vector<ck::index_t> v_gs_os_ns_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
+            : std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
+
+    std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
+    std::vector<ck::index_t> y_gs_ms_os_strides =
+        output_permute
+            ? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
+            : std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
+
+    std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
+    std::vector<ck::index_t> z_gs_ms_ns_strides =
+        input_permute
+            ? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
+            : std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
+    // The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward pass
+    // Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
+    //    = exp(Si) / exp(log(sum(exp() + ...)))
+    //    = exp(Si - log(sum(exp() + ...)))
+    //               ^^^^^^^^^^^^^^^^^^^^^
+    //                       LSE
+    std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
+    std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
+
+    Tensor<InputDataType> q_gs_ms_ks(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
+    Tensor<InputDataType> k_gs_ns_ks(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
+    Tensor<ZDataType> z_fwd_gs_ms_ns(z_gs_ms_ns_lengths, z_gs_ms_ns_strides);
+    Tensor<ZDataType> z_bwd_gs_ms_ns(z_gs_ms_ns_lengths, z_gs_ms_ns_strides);
+    Tensor<InputDataType> v_gs_os_ns(v_gs_os_ns_lengths, v_gs_os_ns_strides);
+    Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
+    Tensor<InputDataType> y_gs_ms_os_device_result(y_gs_ms_os_lengths, y_gs_ms_os_strides);
+    Tensor<LSEDataType> lse_gs_ms_device_result(lse_gs_ms_lengths, lse_gs_ms_strides);
+    Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
+    Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
+    Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
+
+    std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
+    std::cout << "k_gs_ns_ks: " << k_gs_ns_ks.mDesc << std::endl;
+    std::cout << "z_gs_ms_ns: " << z_fwd_gs_ms_ns.mDesc << std::endl;
+    std::cout << "v_gs_os_ns: " << v_gs_os_ns.mDesc << std::endl;
+    std::cout << "y_gs_ms_os: " << y_gs_ms_os_device_result.mDesc << std::endl;
+    std::cout << "lse_gs_ms_os: " << lse_gs_ms_device_result.mDesc << std::endl;
+
+    z_fwd_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
+    z_bwd_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{0});
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<InputDataType>{-2, 2});
+        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<InputDataType>{-2, 2});
+        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<InputDataType>{-2, 2});
+        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_2<InputDataType>{-2, 2});
+        break;
+    case 2:
+        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<InputDataType>{0.0, 1.0});
+        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<InputDataType>{0.0, 1.0});
+        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_3<InputDataType>{-0.5, 0.5});
+        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_3<InputDataType>{-0.5, 0.5});
+        break;
+    case 3:
+        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<InputDataType>{-5, 5});
+        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        break;
+    case 4:
+        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
+        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
+        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
+        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
+        break;
+    case 5:
+        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
+        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
+        // dO dot O = [0; 1; 2; ...]
+        break;
+    case 6:
+        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
+        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
+        // assume mnko = 256
+        // P = softmax(QK) = 0.0039 * ones
+        // O = P V = 0.0039 * ones
+        // dP = dO V = [0, 1, 2, ...; 0, 1, 2, ...; ...]
+        // dO dot O = [127.5; ...]
+        // dS = P * (dP - dO dot O)
+        //
+        break;
+    default:
+        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
+        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
+        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1}); // dy[g0, g1, m, o]
+        // assume mnko = 256
+        // P = softmax(QK) = 0.0039 * ones
+        // O = P V = 0.0039 * ones
+        // dP = dO V = ones
+        // dS = P * (dP - (dO dot O))
+        //    = 0.0039 * ones * (ones - 0.0039*256)
+        //    = 0.0039 * ones * (ones - 1)
+        //    = 0
+    }
+
+    // qkv gradients have the same descriptor as with qkv
+    DeviceMem q_device_buf(sizeof(InputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
+    DeviceMem k_device_buf(sizeof(InputDataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
+    DeviceMem z_fwd_device_buf(sizeof(ZDataType) * z_fwd_gs_ms_ns.mDesc.GetElementSpaceSize());
+    DeviceMem z_bwd_device_buf(sizeof(ZDataType) * z_bwd_gs_ms_ns.mDesc.GetElementSpaceSize());
+    DeviceMem v_device_buf(sizeof(InputDataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
+    DeviceMem y_device_buf(sizeof(InputDataType) *
+                           y_gs_ms_os_device_result.mDesc.GetElementSpaceSize());
+    DeviceMem lse_device_buf(sizeof(LSEDataType) *
+                             lse_gs_ms_device_result.mDesc.GetElementSpaceSize());
+    DeviceMem qgrad_device_buf(sizeof(OutputDataType) *
+                               qgrad_gs_ms_ks_device_result.mDesc.GetElementSpaceSize());
+    DeviceMem kgrad_device_buf(sizeof(OutputDataType) *
+                               kgrad_gs_ns_ks_device_result.mDesc.GetElementSpaceSize());
+    DeviceMem vgrad_device_buf(sizeof(OutputDataType) *
+                               vgrad_gs_os_ns_device_result.mDesc.GetElementSpaceSize());
+    DeviceMem ygrad_device_buf(sizeof(InputDataType) * ygrad_gs_ms_os.mDesc.GetElementSpaceSize());
+
+    q_device_buf.ToDevice(q_gs_ms_ks.mData.data());
+    k_device_buf.ToDevice(k_gs_ns_ks.mData.data());
+    z_fwd_device_buf.ToDevice(z_fwd_gs_ms_ns.mData.data());
+    z_bwd_device_buf.ToDevice(z_bwd_gs_ms_ns.mData.data());
+    v_device_buf.ToDevice(v_gs_os_ns.mData.data());
+    ygrad_device_buf.ToDevice(ygrad_gs_ms_os.mData.data());
+
+    auto gemm_fwd    = DeviceGemmInstanceFWD{};
+    auto invoker_fwd = gemm_fwd.MakeInvoker();
+    auto gemm_bwd    = DeviceGemmInstanceBWD{};
+    auto invoker_bwd = gemm_bwd.MakeInvoker();
+
+    if(time_kernel)
+    {
+        auto argument_fwd = gemm_fwd.MakeArgument(
+            static_cast<InputDataType*>(q_device_buf.GetDeviceBuffer()),
+            static_cast<InputDataType*>(k_device_buf.GetDeviceBuffer()),
+            static_cast<InputDataType*>(v_device_buf.GetDeviceBuffer()),
+            static_cast<InputDataType*>(y_device_buf.GetDeviceBuffer()),
+            static_cast<ZDataType*>(nullptr),
+            static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
+            {}, // std::array<void*, 1> p_acc0_biases;
+            {}, // std::array<void*, 1> p_acc1_biases;
+            q_gs_ms_ks_lengths,
+            q_gs_ms_ks_strides,
+            k_gs_ns_ks_lengths,
+            k_gs_ns_ks_strides,
+            v_gs_os_ns_lengths,
+            v_gs_os_ns_strides,
+            y_gs_ms_os_lengths,
+            y_gs_ms_os_strides,
+            z_gs_ms_ns_lengths,
+            z_gs_ms_ns_strides,
+            lse_gs_ms_lengths,
+            {}, // 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},
+            QKVElementOp{},
+            QKVElementOp{},
+            Scale{alpha},
+            QKVElementOp{},
+            YElementOp{},
+            p_drop,          // dropout ratio
+            {seed, offset}); // dropout random seed and offset, offset should be at least the number
+                             // of elements on a thread
+
+        if(!gemm_fwd.IsSupportedArgument(argument_fwd))
+        {
+            std::cout << gemm_fwd.GetTypeString() << " does not support this problem" << std::endl;
+
+            return 0;
+        }
+
+        float ave_time_fwd = invoker_fwd.Run(argument_fwd, StreamConfig{nullptr, true});
+
+        std::size_t flop_fwd = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
+        std::size_t num_btype_fwd =
+            (sizeof(InputDataType) * M * K + sizeof(InputDataType) * K * N +
+             sizeof(InputDataType) * N * O + sizeof(InputDataType) * M * O) *
+            BatchCount;
+
+        float tflops_fwd = static_cast<float>(flop_fwd) / 1.E9 / ave_time_fwd;
+
+        float gb_per_sec_fwd = num_btype_fwd / 1.E6 / ave_time_fwd;
+
+        std::cout << "FWD Perf: " << ave_time_fwd << " ms, " << tflops_fwd << " TFlops, "
+                  << gb_per_sec_fwd << " GB/s, " << gemm_fwd.GetTypeString() << std::endl;
+
+        // not need output z matrix
+        auto argument_bwd = gemm_bwd.MakeArgument(
+            static_cast<InputDataType*>(q_device_buf.GetDeviceBuffer()),
+            static_cast<InputDataType*>(k_device_buf.GetDeviceBuffer()),
+            static_cast<ZDataType*>(nullptr), // set to nullptr
+            static_cast<InputDataType*>(v_device_buf.GetDeviceBuffer()),
+            static_cast<InputDataType*>(y_device_buf.GetDeviceBuffer()),
+            static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
+            static_cast<InputDataType*>(ygrad_device_buf.GetDeviceBuffer()),
+            static_cast<OutputDataType*>(qgrad_device_buf.GetDeviceBuffer()),
+            static_cast<OutputDataType*>(kgrad_device_buf.GetDeviceBuffer()),
+            static_cast<OutputDataType*>(vgrad_device_buf.GetDeviceBuffer()),
+            {}, // std::array<void*, 1> p_acc0_biases;
+            {}, // std::array<void*, 1> p_acc1_biases;
+            q_gs_ms_ks_lengths,
+            q_gs_ms_ks_strides,
+            k_gs_ns_ks_lengths,
+            k_gs_ns_ks_strides,
+            z_gs_ms_ns_lengths,
+            z_gs_ms_ns_strides,
+            v_gs_os_ns_lengths,
+            v_gs_os_ns_strides,
+            y_gs_ms_os_lengths,
+            y_gs_ms_os_strides,
+            lse_gs_ms_lengths,
+            {}, // 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},
+            QKVElementOp{},
+            QKVElementOp{},
+            Scale{alpha},
+            QKVElementOp{},
+            YElementOp{},
+            p_drop,
+            std::tuple<unsigned long long, unsigned long long>(seed, offset));
+
+        qgrad_device_buf.SetZero();
+        // kgrad_device_buf.SetZero(); // reset global accum buffer and rerun
+        // vgrad_device_buf.SetZero();
+        float ave_time_bwd = invoker_bwd.Run(argument_bwd, StreamConfig{nullptr, true});
+
+        // 5 GEMM ops in total:
+        // S_MNK / dP_MNO Gemm (Gemm0 rcr)
+        // dQ_MKN Gemm (Gemm1 rrr)
+        // dV_NOM / dK_NKM Gemm (Gemm2 crr)
+        // 3x MNK + 2x MNO
+        std::size_t flop_bwd = (size_t(3) * M * N * K + size_t(2) * M * N * O) * 2 * BatchCount;
+        // Q/K/V/Y, dQ/dK/dV/dY, LSE
+        std::size_t num_btype_bwd =
+            (sizeof(InputDataType) * M * K + sizeof(InputDataType) * K * N +
+             sizeof(InputDataType) * N * O + sizeof(InputDataType) * M * O * size_t(2) +
+             sizeof(OutputDataType) * M * K + sizeof(OutputDataType) * K * N +
+             sizeof(OutputDataType) * N * O) *
+                BatchCount +
+            sizeof(LSEDataType) * M * BatchCount;
+
+        float tflops_bwd = static_cast<float>(flop_bwd) / 1.E9 / ave_time_bwd;
+
+        float gb_per_sec_bwd = num_btype_bwd / 1.E6 / ave_time_bwd;
+
+        std::cout << "BWD Perf: " << ave_time_bwd << " ms, " << tflops_bwd << " TFlops, "
+                  << gb_per_sec_bwd << " GB/s, " << gemm_bwd.GetTypeString() << std::endl;
+    }
+
+    bool pass = true;
+    if(do_verification)
+    {
+        Tensor<InputDataType> y_gs_ms_os_host_result(y_gs_ms_os_lengths, y_gs_ms_os_strides);
+        Tensor<LSEDataType> lse_gs_ms_host_result(lse_gs_ms_lengths, lse_gs_ms_strides);
+        Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
+        Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
+        Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
+
+        Tensor<InputDataType> q_g_m_k({BatchCount, M, K});
+        Tensor<InputDataType> k_g_n_k({BatchCount, N, K});
+        Tensor<ZDataType> z_fwd_g_m_n({BatchCount, M, N});
+        Tensor<ZDataType> z_bwd_g_m_n({BatchCount, M, N});
+        Tensor<InputDataType> v_g_n_o({BatchCount, N, O});
+        Tensor<AccDataType> s_g_m_n({BatchCount, M, N});
+        Tensor<InputDataType> p_g_m_n({BatchCount, M, N});
+        Tensor<InputDataType> p_drop_g_m_n({BatchCount, M, N});
+        Tensor<InputDataType> y_g_m_o({BatchCount, M, O});
+        Tensor<LSEDataType> lse_g_m({BatchCount, M});
+
+        Tensor<OutputDataType> qgrad_g_m_k({BatchCount, M, K});
+        Tensor<OutputDataType> kgrad_g_n_k({BatchCount, N, K});
+        Tensor<OutputDataType> vgrad_g_n_o({BatchCount, N, O});
+        Tensor<InputDataType> sgrad_g_m_n({BatchCount, M, N});
+        Tensor<InputDataType> pgrad_g_m_n({BatchCount, M, N});
+        Tensor<InputDataType> pgrad_drop_g_m_n({BatchCount, M, N});
+        Tensor<InputDataType> ygrad_g_m_o({BatchCount, M, O});
+        Tensor<InputDataType> ygrad_dot_y_g_m({BatchCount, M});
+
+        // get kernel output matrixes
+        {
+            auto argument_fwd = gemm_fwd.MakeArgument(
+                static_cast<InputDataType*>(q_device_buf.GetDeviceBuffer()),
+                static_cast<InputDataType*>(k_device_buf.GetDeviceBuffer()),
+                static_cast<InputDataType*>(v_device_buf.GetDeviceBuffer()),
+                static_cast<InputDataType*>(y_device_buf.GetDeviceBuffer()),
+                static_cast<ZDataType*>(z_fwd_device_buf.GetDeviceBuffer()),
+                static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
+                {}, // std::array<void*, 1> p_acc0_biases;
+                {}, // std::array<void*, 1> p_acc1_biases;
+                q_gs_ms_ks_lengths,
+                q_gs_ms_ks_strides,
+                k_gs_ns_ks_lengths,
+                k_gs_ns_ks_strides,
+                v_gs_os_ns_lengths,
+                v_gs_os_ns_strides,
+                y_gs_ms_os_lengths,
+                y_gs_ms_os_strides,
+                z_gs_ms_ns_lengths,
+                z_gs_ms_ns_strides,
+                lse_gs_ms_lengths,
+                {}, // 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},
+                QKVElementOp{},
+                QKVElementOp{},
+                Scale{alpha},
+                QKVElementOp{},
+                YElementOp{},
+                p_drop,          // dropout ratio
+                {seed, offset}); // dropout random seed and offset, offset should be at least the
+                                 // number of elements on a thread
+
+            if(!gemm_fwd.IsSupportedArgument(argument_fwd))
+            {
+                std::cout << gemm_fwd.GetTypeString() << " does not support this problem"
+                          << std::endl;
+
+                return 0;
+            }
+            invoker_fwd.Run(argument_fwd, StreamConfig{nullptr, false});
+
+            // copy fwd matirx data form device
+            z_fwd_device_buf.FromDevice(z_fwd_gs_ms_ns.mData.data());
+            y_device_buf.FromDevice(y_gs_ms_os_device_result.mData.data());
+            lse_device_buf.FromDevice(lse_gs_ms_device_result.mData.data());
+
+            // std::cout << "z_fwd_gs_ms_ns ref:\n" << z_fwd_gs_ms_ns;
+            std::ofstream fwd_file("./z_fwd_matrix_txt");
+            fwd_file << z_fwd_gs_ms_ns << std::endl;
+
+            qgrad_device_buf.SetZero();
+            // kgrad_device_buf.SetZero();
+            // vgrad_device_buf.SetZero();
+
+            auto argument_bwd = gemm_bwd.MakeArgument(
+                static_cast<InputDataType*>(q_device_buf.GetDeviceBuffer()),
+                static_cast<InputDataType*>(k_device_buf.GetDeviceBuffer()),
+                static_cast<ZDataType*>(z_bwd_device_buf.GetDeviceBuffer()),
+                static_cast<InputDataType*>(v_device_buf.GetDeviceBuffer()),
+                static_cast<InputDataType*>(y_device_buf.GetDeviceBuffer()),
+                static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
+                static_cast<InputDataType*>(ygrad_device_buf.GetDeviceBuffer()),
+                static_cast<OutputDataType*>(qgrad_device_buf.GetDeviceBuffer()),
+                static_cast<OutputDataType*>(kgrad_device_buf.GetDeviceBuffer()),
+                static_cast<OutputDataType*>(vgrad_device_buf.GetDeviceBuffer()),
+                {}, // std::array<void*, 1> p_acc0_biases;
+                {}, // std::array<void*, 1> p_acc1_biases;
+                q_gs_ms_ks_lengths,
+                q_gs_ms_ks_strides,
+                k_gs_ns_ks_lengths,
+                k_gs_ns_ks_strides,
+                z_gs_ms_ns_lengths,
+                z_gs_ms_ns_strides,
+                v_gs_os_ns_lengths,
+                v_gs_os_ns_strides,
+                y_gs_ms_os_lengths,
+                y_gs_ms_os_strides,
+                lse_gs_ms_lengths,
+                {}, // 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},
+                QKVElementOp{},
+                QKVElementOp{},
+                Scale{alpha},
+                QKVElementOp{},
+                YElementOp{},
+                p_drop,
+                std::tuple<unsigned long long, unsigned long long>(seed, offset));
+
+            if(!gemm_bwd.IsSupportedArgument(argument_bwd))
+            {
+                std::cout << gemm_bwd.GetTypeString() << " does not support this problem"
+                          << std::endl;
+
+                return 0;
+            }
+            invoker_bwd.Run(argument_bwd, StreamConfig{nullptr, false});
+
+            // copy bwd matirx data form device
+            z_bwd_device_buf.FromDevice(z_bwd_gs_ms_ns.mData.data());
+            qgrad_device_buf.FromDevice(qgrad_gs_ms_ks_device_result.mData.data());
+            kgrad_device_buf.FromDevice(kgrad_gs_ns_ks_device_result.mData.data());
+            vgrad_device_buf.FromDevice(vgrad_gs_os_ns_device_result.mData.data());
+
+            // std::cout << "z_bwd_gs_ms_ns ref:\n" << z_bwd_gs_ms_ns;
+            std::ofstream bwd_file("./z_bwd_matrix_txt");
+            bwd_file << z_bwd_gs_ms_ns << std::endl;
+        }
+
+        q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
+            q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+        });
+        k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
+            k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+        });
+        v_gs_os_ns.ForEach([&](auto& self, auto idx) {
+            v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
+        });
+        z_fwd_gs_ms_ns.ForEach([&](auto& self, auto idx) {
+            z_fwd_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+        });
+
+        run_attention_fwd_host(q_g_m_k,
+                               k_g_n_k,
+                               v_g_n_o,
+                               alpha,
+                               s_g_m_n,
+                               p_g_m_n,
+                               y_g_m_o,
+                               lse_g_m,
+                               p_drop_g_m_n,
+                               z_fwd_g_m_n,
+                               p_dropout_in_16bits,
+                               rp_dropout);
+
+        ygrad_gs_ms_os.ForEach([&](auto& self, auto idx) {
+            ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+        });
+        z_bwd_gs_ms_ns.ForEach([&](auto& self, auto idx) {
+            z_bwd_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
+        });
+
+#if PRINT_HOST
+        {
+            std::cout << "q_g_m_k ref:\n" << q_g_m_k;
+            std::cout << "k_g_n_k ref:\n" << k_g_n_k;
+            std::cout << "v_g_n_o ref:\n" << v_g_n_o;
+            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
+        }
+#endif
+
+        // Gradients
+        auto ref_gemm0_grad         = ReferenceGemm0GradInstance{};
+        auto ref_gemm0_grad_invoker = ref_gemm0_grad.MakeInvoker();
+        using RefGemm0GradArg       = ReferenceGemm0GradInstance::Argument;
+        auto ref_gemm1_grad         = ReferenceGemm1GradInstance{};
+        auto ref_gemm1_grad_invoker = ref_gemm1_grad.MakeInvoker();
+        using RefGemm1GradArg       = ReferenceGemm1GradInstance::Argument;
+
+        // dP_dropout = dY * V^T
+        auto v_g_o_n = v_g_n_o.Transpose({0, 2, 1});
+        ref_gemm0_grad_invoker.Run(RefGemm0GradArg{
+            ygrad_g_m_o, v_g_o_n, pgrad_drop_g_m_n, PassThrough{}, PassThrough{}, Scale{1.f}});
+#if PRINT_HOST
+        {
+            std::cout << "===== dP = dY * V^T\n";
+            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
+            std::cout << "v_g_o_n ref:\n" << v_g_o_n;
+            std::cout << "pgrad_drop_g_m_n ref:\n" << pgrad_drop_g_m_n;
+        }
+#endif
+        // dP = dP_dropout x Z
+        auto ref_dropout         = ReferenceDropoutInstance{};
+        auto ref_dropout_invoker = ref_dropout.MakeInvoker();
+        auto ref_dropout_argment = ref_dropout.MakeArgument(
+            z_bwd_g_m_n, pgrad_drop_g_m_n, pgrad_g_m_n, p_dropout_in_16bits, rp_dropout);
+        ref_dropout_invoker.Run(ref_dropout_argment);
+
+        // dS_i_j = P_i_j .* (dP_i_j - dY_i dot Y_i)
+        sgrad_g_m_n.ForEach([&](auto& self, auto idx_gmn) {
+            float ygrad_dot_y = 0;
+            for(int o = 0; o < O; o++)
+            {
+                auto idx_gmo = idx_gmn;
+                idx_gmo[2]   = o;
+                ygrad_dot_y += ck::type_convert<AccDataType>(ygrad_g_m_o(idx_gmo)) *
+                               ck::type_convert<AccDataType>(y_g_m_o(idx_gmo));
+            }
+            self(idx_gmn) = ck::type_convert<InputDataType>(
+                ck::type_convert<AccDataType>(p_g_m_n(idx_gmn)) *
+                (ck::type_convert<AccDataType>(pgrad_g_m_n(idx_gmn)) - ygrad_dot_y));
+        });
+#if PRINT_HOST
+        {
+            std::cout << "===== dS_i_j = P_i_j .* (dP_i_j - dY_i dot Y_i)\n";
+            std::cout << "p_g_m_n ref:\n" << p_g_m_n;
+            std::cout << "pgrad_g_m_n ref:\n" << pgrad_g_m_n;
+            std::cout << "y_g_m_o ref:\n" << y_g_m_o;
+            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
+            std::cout << "sgrad_g_m_n ref:\n" << sgrad_g_m_n;
+        }
+#endif
+        // dV = P_drop^T * dY
+        auto p_drop_g_n_m = p_drop_g_m_n.Transpose({0, 2, 1});
+        ref_gemm1_grad_invoker.Run(RefGemm1GradArg{
+            p_drop_g_n_m, ygrad_g_m_o, vgrad_g_n_o, PassThrough{}, PassThrough{}, Scale{1.0f}});
+#if PRINT_HOST
+        {
+            std::cout << "===== dV = P^T * dY\n";
+            std::cout << "p_drop_g_n_m ref:\n" << p_drop_g_n_m;
+            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
+            std::cout << "vgrad_g_n_o ref:\n" << vgrad_g_n_o;
+        }
+#endif
+
+        // dQ = alpha * dS * K
+        ref_gemm1_grad_invoker.Run(RefGemm1GradArg{
+            sgrad_g_m_n, k_g_n_k, qgrad_g_m_k, PassThrough{}, PassThrough{}, Scale{alpha}});
+#if PRINT_HOST
+        {
+            std::cout << "===== dQ = alpha * dS * K\n";
+            std::cout << "sgrad_g_m_n ref:\n" << sgrad_g_m_n;
+            std::cout << "k_g_n_k ref:\n" << k_g_n_k;
+            std::cout << "qgrad_g_m_k ref:\n" << qgrad_g_m_k;
+        }
+#endif
+
+        // dK = alpha * dS^T * Q
+        auto sgrad_g_n_m = sgrad_g_m_n.Transpose({0, 2, 1});
+        ref_gemm1_grad_invoker.Run(RefGemm1GradArg{
+            sgrad_g_n_m, q_g_m_k, kgrad_g_n_k, PassThrough{}, PassThrough{}, Scale{alpha}});
+#if PRINT_HOST
+        {
+            std::cout << "===== dK = alpha * dS^T * Q\n";
+            std::cout << "sgrad_g_n_m ref:\n" << sgrad_g_n_m;
+            std::cout << "q_g_m_k ref:\n" << q_g_m_k;
+            std::cout << "kgrad_g_n_k ref:\n" << kgrad_g_n_k;
+        }
+#endif
+
+        // permute
+        y_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) = y_g_m_o(g, idx[2], idx[3]);
+        });
+        lse_gs_ms_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) = lse_g_m(g, idx[2]);
+        });
+        qgrad_gs_ms_ks_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) = qgrad_g_m_k(g, idx[2], idx[3]);
+        });
+        kgrad_gs_ns_ks_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) = kgrad_g_n_k(g, idx[2], idx[3]);
+        });
+        vgrad_gs_os_ns_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) = vgrad_g_n_o(g, idx[3], idx[2]);
+        });
+
+        // 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<InputDataType, ck::bhalf_t> || std::is_same_v<GemmDataType, ck::bhalf_t>)
+        {
+            rtol = 1e-2;
+            atol = 1e-2;
+        }
+
+        std::cout << "Checking z:\n";
+        pass &= ck::utils::check_err(z_fwd_gs_ms_ns.mData, z_bwd_gs_ms_ns.mData, 1);
+
+        std::cout << "Checking y:\n";
+        pass &= ck::utils::check_err(
+            y_gs_ms_os_device_result.mData, y_gs_ms_os_host_result.mData, "error", rtol, atol);
+
+        std::cout << "Checking lse:\n";
+        pass &= ck::utils::check_err(
+            lse_gs_ms_device_result.mData, lse_gs_ms_host_result.mData, "error", rtol, atol);
+
+        std::cout << "Checking qgrad:\n";
+        pass &= ck::utils::check_err(qgrad_gs_ms_ks_device_result.mData,
+                                     qgrad_gs_ms_ks_host_result.mData,
+                                     "error",
+                                     1e-2,
+                                     1e-2);
+        std::cout << "Checking kgrad:\n";
+        pass &= ck::utils::check_err(kgrad_gs_ns_ks_device_result.mData,
+                                     kgrad_gs_ns_ks_host_result.mData,
+                                     "error",
+                                     1e-2,
+                                     1e-2);
+        std::cout << "Checking vgrad:\n";
+        pass &= ck::utils::check_err(vgrad_gs_os_ns_device_result.mData,
+                                     vgrad_gs_os_ns_host_result.mData,
+                                     "error",
+                                     1e-2,
+                                     1e-2);
+    }
+
+    return pass ? ((void)(std::cout << "pass\n"), 0) : ((void)(std::cout << "fail\n"), 1);
+}
+
+int main(int argc, char* argv[]) { return run(argc, argv); }

include/ck/tensor_operation/gpu/block/blockwise_dropout.hpp

@@ -122,6 +122,160 @@ struct BlockwiseDropout
         });
     }
 
+
+        template <typename CThreadBuffer, bool using_sign_bit = false>
+    __host__ __device__ void ApplyDropoutAttnBwd(CThreadBuffer& in_thread_buf,
+                                                 ck::philox& ph,
+                                                 index_t element_global_1d_id,
+                                                 index_t MRaw)
+    {
+
+        auto execute_dropout = [&](bool keep, DataType val) {
+            if constexpr(using_sign_bit)
+                return keep ? val : -val;
+            else
+                return keep ? val * p_dropout_rescale : float(0);
+        };
+
+        constexpr int tmp_size = MRepeat * KRepeat;
+
+        int philox_calls = tmp_size / 4;
+
+        ushort tmp[tmp_size];
+        for(int i = 0; i < philox_calls; i++)
+        {
+            ph.get_random_4x16((tmp + i * 4), element_global_1d_id + i * 8 * MRaw);
+        }
+
+        block_sync_lds();
+
+        int tmp_index = 0;
+        static_for<0, MRepeat, 1>{}([&](auto iM) {
+            static_for<0, KRepeat, 1>{}([&](auto iK) {
+                auto offset = Number<ThreadSliceDesc_M_K{}.CalculateOffset(make_tuple(iM, iK))>{};
+                in_thread_buf(offset) =
+                    execute_dropout(tmp[tmp_index] <= p_dropout_16bits, in_thread_buf(offset));
+                tmp_index = tmp_index + 1;
+            });
+        });
+    }
+
+    template <typename CThreadBuffer, typename ZThreadBuffer, bool using_sign_bit = false>
+    __host__ __device__ void ApplyDropoutAttnBwdSaveZ(CThreadBuffer& in_thread_buf,
+                                                      ck::philox& ph,
+                                                      index_t element_global_1d_id,
+                                                      ZThreadBuffer& z_thread_buf,
+                                                      index_t MRaw)
+    {
+
+        auto execute_dropout = [&](bool keep, DataType val) {
+            if constexpr(using_sign_bit)
+                return keep ? val : -val;
+            else
+                return keep ? val * p_dropout_rescale : float(0);
+        };
+
+        constexpr int tmp_size = MRepeat * KRepeat;
+
+        int philox_calls = tmp_size / 4;
+
+        ushort tmp[tmp_size];
+        for(int i = 0; i < philox_calls; i++)
+        {
+            ph.get_random_4x16((tmp + i * 4), element_global_1d_id + i * 8 * MRaw);
+        }
+
+        // ushort tmp_id[tmp_size];
+        // for(int i = 0; i < philox_calls; i++)
+        //{
+        //    for(int j = 0; j < 4; j++)
+        //    {
+        //        tmp_id[i * 4 + j] = element_global_1d_id + i * 8 * MRaw;
+        //    }
+        //}
+
+        block_sync_lds();
+
+        int tmp_index = 0;
+        static_for<0, MRepeat, 1>{}([&](auto iM) {
+            static_for<0, KRepeat, 1>{}([&](auto iK) {
+                auto offset = Number<ThreadSliceDesc_M_K{}.CalculateOffset(make_tuple(iM, iK))>{};
+                in_thread_buf(offset) =
+                    execute_dropout(tmp[tmp_index] <= p_dropout_16bits, in_thread_buf(offset));
+                z_thread_buf(offset) = tmp[tmp_index];
+                tmp_index            = tmp_index + 1;
+            });
+        });
+    }
+
+    template <typename CThreadBuffer, typename ZThreadBuffer, bool using_sign_bit = false>
+    __host__ __device__ void ApplyDropoutWithZ(CThreadBuffer& in_thread_buf,
+                                               ZThreadBuffer& z_thread_buf)
+    {
+
+        auto execute_dropout = [&](bool keep, DataType val) {
+            if constexpr(using_sign_bit)
+                return keep ? val : -val;
+            else
+                return keep ? val * p_dropout_rescale : float(0);
+        };
+
+        int tmp_index = 0;
+        static_for<0, MRepeat, 1>{}([&](auto iM) {
+            static_for<0, KRepeat, 1>{}([&](auto iK) {
+                auto offset = Number<ThreadSliceDesc_M_K{}.CalculateOffset(make_tuple(iM, iK))>{};
+                in_thread_buf(offset) = execute_dropout(z_thread_buf(offset) <= p_dropout_16bits,
+                                                        in_thread_buf(offset));
+                tmp_index             = tmp_index + 1;
+                // if(get_thread_global_1d_id()==0){
+                //    printf("z at %d is %u \n", tmp_index, z_thread_buf(offset));
+                //}
+            });
+        });
+    }
+
+    // get raw z matrix with random number for shuffle
+    template <typename ZThreadBuffer>
+    __host__ __device__ void GenerateZMatrixAttnFwd(ck::philox& ph,
+                                                    index_t element_global_1d_id,
+                                                    ZThreadBuffer& z_thread_buf)
+    {
+
+        // if(get_thread_global_1d_id() == 0){
+        //     printf("MRepeat & KRepeat is %d , %d . \n", MRepeat, KRepeat);
+        // }
+
+        constexpr int tmp_size = MRepeat * KRepeat;
+
+        int philox_calls = tmp_size / 4;
+
+        ushort tmp[tmp_size];
+        for(int i = 0; i < philox_calls; i++)
+        {
+            ph.get_random_4x16((tmp + i * 4), element_global_1d_id + i * 8);
+        }
+
+        // ushort tmp_id[tmp_size];
+        // for(int i = 0; i < philox_calls; i++)
+        //{
+        //    for(int j = 0; j < 4; j++)
+        //    {
+        //        tmp_id[i * 4 + j] = element_global_1d_id + i * 8;
+        //    }
+        //}
+
+        block_sync_lds();
+
+        int tmp_index = 0;
+        static_for<0, MRepeat, 1>{}([&](auto iM) {
+            static_for<0, KRepeat, 1>{}([&](auto iK) {
+                auto offset = Number<ThreadSliceDesc_M_K{}.CalculateOffset(make_tuple(iM, iK))>{};
+                z_thread_buf(offset) = tmp[tmp_index];
+                tmp_index            = tmp_index + 1;
+            });
+        });
+    }
+
     ushort p_dropout_16bits;
     DataType p_dropout_rescale;
 };

include/ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle_v4.hpp

@@ -16,7 +16,7 @@
 #include "ck/tensor_operation/gpu/device/masking_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_backward_xdl_cshuffle_pt6.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_backward_xdl_cshuffle_pt4.hpp"
 #include "ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
@@ -39,7 +39,7 @@ template <typename GridwiseGemm,
           typename CElementwiseOperation,
           typename AGridDesc_AK0_M_AK1,
           typename BGridDesc_BK0_N_BK1,
-          typename ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5,
+          typename ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3,
           typename B1GridDesc_BK0_N_BK1,
           typename YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock,
           typename LSEGridDescriptor_M,
@@ -71,7 +71,7 @@ __global__ void
             const CElementwiseOperation c_element_op,
             const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
             const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
-            const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5
+            const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
                 c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
             const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
             const YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
@@ -85,7 +85,9 @@ __global__ void
             const C0MatrixMask c0_matrix_mask,
             const float p_drop,
             const unsigned long long seed,
-            const unsigned long long offset)
+            const unsigned long long offset,
+            const index_t MRaw,
+            const index_t NRaw)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
@@ -144,6 +146,9 @@ __global__ void
                 c0_matrix_mask,
                 p_drop,
                 ph,
+                g_idx,
+                MRaw,
+                NRaw,
                 i);
         }
     }
@@ -176,6 +181,9 @@ __global__ void
                                                       c0_matrix_mask,
                                                       p_drop,
                                                       ph,
+                                                      g_idx,
+                                                      MRaw,
+                                                      NRaw,
                                                       0);
     }
 #else
@@ -278,6 +286,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
     // TODO: implement bias combination
     static_assert(NumAcc0Bias == 0 && NumAcc0Bias == 0, "Bias addition is unimplemented");
 
+#if 0
+    // TODO: use alias
+    static constexpr index_t NumDimGemm0M = NumDimM;
+    static constexpr index_t NumDimGemm0N = NumDimN;
+    static constexpr index_t NumDimGemm0K = NumDimK;
+    static constexpr index_t NumDimGemm1M = NumDimM;
+    static constexpr index_t NumDimGemm1N = NumDimO;
+    static constexpr index_t NumDimGemm1K = NumDimN;
+#endif
+
     using DeviceOp = DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1;
 
     static constexpr auto I0 = Number<0>{};
@@ -808,8 +826,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
             seed_   = std::get<0>(seeds);
             offset_ = std::get<1>(seeds);
 
-            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_ =
-                GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(z_grid_desc_m_n_);
+            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_ =
+                GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(z_grid_desc_m_n_);
             // Print();
         }
 
@@ -869,8 +887,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
         typename GridwiseGemm::YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
             y_grid_desc_mblock_mperblock_oblock_operblock_;
 
-        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5
-            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_;
+        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
+            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_;
 
         // block-to-c-tile map
         typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
@@ -933,7 +951,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
                     CElementwiseOperation,
                     DeviceOp::AGridDesc_AK0_M_AK1,
                     DeviceOp::BGridDesc_BK0_N_BK1,
-                    typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5,
+                    typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3,
                     DeviceOp::B1GridDesc_BK0_N_BK1,
                     typename GridwiseGemm::YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock,
                     DeviceOp::LSEGridDesc_M,
@@ -967,7 +985,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
                     arg.c_element_op_,
                     arg.a_grid_desc_ak0_m_ak1_,
                     arg.b_grid_desc_bk0_n_bk1_,
-                    arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_,
+                    arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_,
                     arg.b1_grid_desc_bk0_n_bk1_,
                     arg.y_grid_desc_mblock_mperblock_oblock_operblock_,
                     arg.lse_grid_desc_m_,
@@ -979,11 +997,24 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
                     arg.c0_matrix_mask_,
                     arg.p_drop_,
                     arg.seed_,
-                    arg.offset_);
+                    arg.offset_,
+                    arg.raw_lengths_mz_nz_kz_gemm1nz_[0],
+                    arg.raw_lengths_mz_nz_kz_gemm1nz_[1]);
             };
 
+            // Gemm1_K is split into Gemm1_K0/K1 where K1 is known at compile time, so we only need
+            // to concern Gemm0's loop
+#if 1
+            // if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            // {
+            //     ave_time = launch_kernel(integral_constant<bool, true>{});
+            // }
+            // else
+            // {
+            //     ave_time = launch_kernel(integral_constant<bool, false>{});
+            // }
             ave_time = launch_kernel(integral_constant<bool, false>{});
-
+#endif
             return ave_time;
         }
 
@@ -1003,6 +1034,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
 
     static bool IsSupportedArgument(const Argument& arg)
     {
+#if 0
+        arg.Print();
+#endif
+
         if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
         {
             return false;

include/ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle_v5.hpp

@@ -15,7 +15,7 @@
 #include "ck/tensor_operation/gpu/device/masking_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_backward_xdl_cshuffle_pt7.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_backward_xdl_cshuffle_pt5.hpp"
 #include "ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
@@ -38,7 +38,7 @@ template <typename GridwiseGemm,
           typename CElementwiseOperation,
           typename AGridDesc_AK0_M_AK1,
           typename BGridDesc_BK0_N_BK1,
-          typename ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5,
+          typename ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3,
           typename B1GridDesc_BK0_N_BK1,
           typename YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock,
           typename LSEGridDescriptor_M,
@@ -70,8 +70,8 @@ __global__ void
             const CElementwiseOperation c_element_op,
             const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
             const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
-            const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5
-                c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+            const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
+                c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
             const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
             const YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
                 c_grid_desc_mblock_mperblock_nblock_nperblock,
@@ -84,7 +84,9 @@ __global__ void
             const C0MatrixMask c0_matrix_mask,
             const float p_drop,
             const unsigned long long seed,
-            const unsigned long long offset)
+            const unsigned long long offset,
+            const index_t MRaw,
+            const index_t NRaw)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
@@ -134,7 +136,7 @@ __global__ void
                 c_element_op,
                 a_grid_desc_ak0_m_ak1,
                 b_grid_desc_bk0_n_bk1,
-                c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
                 b1_grid_desc_bk0_n_bk1,
                 c_grid_desc_mblock_mperblock_nblock_nperblock,
                 lse_grid_desc_m,
@@ -143,6 +145,9 @@ __global__ void
                 c0_matrix_mask,
                 p_drop,
                 ph,
+                g_idx,
+                MRaw,
+                NRaw,
                 i);
         }
     }
@@ -166,7 +171,7 @@ __global__ void
                                                       c_element_op,
                                                       a_grid_desc_ak0_m_ak1,
                                                       b_grid_desc_bk0_n_bk1,
-                                                      c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                                                      c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
                                                       b1_grid_desc_bk0_n_bk1,
                                                       c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                       lse_grid_desc_m,
@@ -175,6 +180,9 @@ __global__ void
                                                       c0_matrix_mask,
                                                       p_drop,
                                                       ph,
+                                                      g_idx,
+                                                      MRaw,
+                                                      NRaw,
                                                       0);
     }
 #else
@@ -284,6 +292,16 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
     // TODO: implement bias combination
     static_assert(NumAcc0Bias == 0 && NumAcc0Bias == 0, "Bias addition is unimplemented");
 
+#if 0
+    // TODO: use alias
+    static constexpr index_t NumDimGemm0M = NumDimM;
+    static constexpr index_t NumDimGemm0N = NumDimN;
+    static constexpr index_t NumDimGemm0K = NumDimK;
+    static constexpr index_t NumDimGemm1M = NumDimM;
+    static constexpr index_t NumDimGemm1N = NumDimO;
+    static constexpr index_t NumDimGemm1K = NumDimN;
+#endif
+
     using DeviceOp = DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2;
 
     static constexpr auto I0 = Number<0>{};
@@ -821,8 +839,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
             seed_   = std::get<0>(seeds);
             offset_ = std::get<1>(seeds);
 
-            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_ =
-                GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(z_grid_desc_m_n_);
+            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_ =
+                GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(z_grid_desc_m_n_);
             // Print();
         }
 
@@ -882,8 +900,8 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
         typename GridwiseGemm::YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
             y_grid_desc_mblock_mperblock_oblock_operblock_;
 
-        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5
-            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_;
+        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
+            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_;
 
         // block-to-c-tile map
         typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
@@ -950,7 +968,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
                     CElementwiseOperation,
                     DeviceOp::AGridDesc_AK0_M_AK1,
                     DeviceOp::BGridDesc_BK0_N_BK1,
-                    typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5,
+                    typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3,
                     DeviceOp::B1GridDesc_BK0_N_BK1,
                     typename GridwiseGemm::YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock,
                     DeviceOp::LSEGridDesc_M,
@@ -984,7 +1002,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
                     arg.c_element_op_,
                     arg.a_grid_desc_ak0_m_ak1_,
                     arg.b_grid_desc_bk0_n_bk1_,
-                    arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_,
+                    arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_,
                     arg.b1_grid_desc_bk0_n_bk1_,
                     arg.y_grid_desc_mblock_mperblock_oblock_operblock_,
                     arg.lse_grid_desc_m_,
@@ -996,11 +1014,14 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
                     arg.c0_matrix_mask_,
                     arg.p_drop_,
                     arg.seed_,
-                    arg.offset_);
+                    arg.offset_,
+                    arg.raw_lengths_mz_nz_kz_gemm1nz_[0],
+                    arg.raw_lengths_mz_nz_kz_gemm1nz_[1]);
             };
 
             // Gemm1_K is split into Gemm1_K0/K1 where K1 is known at compile time, so we only need
             // to concern Gemm0's loop
+#if 1
             if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
             {
                 ave_time = launch_kernel(integral_constant<bool, true>{});
@@ -1009,7 +1030,7 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
             {
                 ave_time = launch_kernel(integral_constant<bool, false>{});
             }
-
+#endif
             return ave_time;
         }
 
@@ -1029,6 +1050,10 @@ struct DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
 
     static bool IsSupportedArgument(const Argument& arg)
     {
+#if 0
+        arg.Print();
+#endif
+
         if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
         {
             return false;

include/ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_forward_xdl_cshuffle_v2.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#include "ck/utility/common_header.hpp"
+#include "ck/utility/philox_rand.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_forward_xdl_cshuffle_pt2.hpp"
+#include "ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatC,
+          typename ZDataType,
+          typename FloatLSE,
+          typename GemmAccDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename AccElementwiseOperation,
+          typename B1ElementwiseOperation,
+          typename CElementwiseOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename B1GridDesc_BK0_N_BK1,
+          typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5,
+          typename LSEGridDescriptor_M,
+          typename Block2CTileMap,
+          typename ComputeBasePtrOfStridedBatch,
+          typename C0MatrixMask,
+          bool HasMainKBlockLoop,
+          bool IsDropout,
+          bool IsLseStoring,
+          bool Deterministic>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_batched_multiheadattention_forward_xdl_cshuffle(
+            const FloatAB* __restrict__ p_a_grid,
+            const FloatAB* __restrict__ p_b_grid,
+            const FloatAB* __restrict__ p_b1_grid,
+            FloatC* __restrict__ p_c_grid,
+            ZDataType* __restrict__ p_z_grid,
+            FloatLSE* __restrict__ p_lse_grid,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const AccElementwiseOperation acc_element_op,
+            const B1ElementwiseOperation b1_element_op,
+            const CElementwiseOperation c_element_op,
+            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
+            const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
+            const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5
+                z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+            const LSEGridDescriptor_M lse_grid_desc_m,
+            const Block2CTileMap block_2_ctile_map,
+            const index_t batch_count,
+            const index_t mblock,
+            const ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch,
+            const C0MatrixMask c0_matrix_mask,
+            const ushort p_dropout_in_16bits,
+            const GemmAccDataType p_dropout_rescale,
+            const unsigned long long seed,
+            const unsigned long long offset,
+            const index_t MRaw,
+            const index_t NRaw)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    __shared__ char p_shared[GridwiseGemm::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 b_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(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)));
+    const long_index_t z_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetZBasePtr(g_idx)));
+    const long_index_t lse_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetLSEBasePtr(g_idx)));
+
+    const index_t global_thread_id = get_thread_global_1d_id();
+    ck::philox ph(seed, global_thread_id, offset);
+
+    if constexpr(Deterministic)
+    {
+        for(index_t i = 0; i < mblock; i++)
+        {
+            GridwiseGemm::template Run<HasMainKBlockLoop, IsDropout, IsLseStoring>(
+                p_a_grid + a_batch_offset,
+                p_b_grid + b_batch_offset,
+                p_b1_grid + b1_batch_offset,
+                p_c_grid + c_batch_offset,
+                nullptr ? nullptr : p_z_grid + z_batch_offset,
+                nullptr ? nullptr : p_lse_grid + lse_batch_offset,
+                p_shared,
+                a_element_op,
+                b_element_op,
+                acc_element_op,
+                b1_element_op,
+                c_element_op,
+                a_grid_desc_ak0_m_ak1,
+                b_grid_desc_bk0_n_bk1,
+                b1_grid_desc_bk0_n_bk1,
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+                z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                lse_grid_desc_m,
+                block_2_ctile_map,
+                c0_matrix_mask,
+                p_dropout_in_16bits,
+                p_dropout_rescale,
+                ph,
+                g_idx,
+                MRaw,
+                NRaw,
+                i);
+        }
+    }
+    else
+    {
+        GridwiseGemm::template Run<HasMainKBlockLoop, IsDropout, IsLseStoring>(
+            p_a_grid + a_batch_offset,
+            p_b_grid + b_batch_offset,
+            p_b1_grid + b1_batch_offset,
+            p_c_grid + c_batch_offset,
+            nullptr ? nullptr : p_z_grid + z_batch_offset,
+            nullptr ? nullptr : p_lse_grid + lse_batch_offset,
+            p_shared,
+            a_element_op,
+            b_element_op,
+            acc_element_op,
+            b1_element_op,
+            c_element_op,
+            a_grid_desc_ak0_m_ak1,
+            b_grid_desc_bk0_n_bk1,
+            b1_grid_desc_bk0_n_bk1,
+            c_grid_desc_mblock_mperblock_nblock_nperblock,
+            z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+            lse_grid_desc_m,
+            block_2_ctile_map,
+            c0_matrix_mask,
+            p_dropout_in_16bits,
+            p_dropout_rescale,
+            ph,
+            g_idx,
+            MRaw,
+            NRaw,
+            0);
+    }
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_b1_grid;
+    ignore = p_c_grid;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = acc_element_op;
+    ignore = b1_element_op;
+    ignore = c_element_op;
+    ignore = a_grid_desc_ak0_m_ak1;
+    ignore = b_grid_desc_bk0_n_bk1;
+    ignore = b1_grid_desc_bk0_n_bk1;
+    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = block_2_ctile_map;
+    ignore = batch_count;
+    ignore = compute_base_ptr_of_batch;
+    ignore = c0_matrix_mask;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+// Computes C = A * B0 * B1
+//              ^^^^^^ (Acc0)
+//              ^^^^^^^^^^^ (Acc1)
+template <index_t NumDimG,
+          index_t NumDimM,
+          index_t NumDimN,
+          index_t NumDimK,
+          index_t NumDimO, // NumDimGemm1N
+          typename ADataType,
+          typename BDataType,
+          typename B1DataType,
+          typename CDataType,
+          typename GemmDataType,
+          typename ZDataType,
+          typename LSEDataType,
+          typename Acc0BiasDataType,
+          typename Acc1BiasDataType,
+          typename GemmAccDataType,
+          typename CShuffleDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename AccElementwiseOperation,
+          typename B1ElementwiseOperation,
+          typename CElementwiseOperation,
+          GemmSpecialization GemmSpec,
+          TensorSpecialization ASpec,
+          TensorSpecialization BSpec,
+          TensorSpecialization B1Spec,
+          TensorSpecialization CSpec,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock, // Gemm0NPerBlock
+          index_t KPerBlock, // Gemm0KPerBlock
+          index_t Gemm1NPerBlock,
+          index_t Gemm1KPerBlock,
+          index_t AK1,
+          index_t BK1,
+          index_t B1K1,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          index_t Gemm1NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BBlockLdsExtraN,
+          typename B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename B1BlockTransferThreadClusterArrangeOrder,
+          typename B1BlockTransferSrcAccessOrder,
+          index_t B1BlockTransferSrcVectorDim,
+          index_t B1BlockTransferSrcScalarPerVector,
+          index_t B1BlockTransferDstScalarPerVector_BK1,
+          bool B1BlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          MaskingSpecialization MaskingSpec,
+          bool Deterministic,
+          LoopScheduler LoopSched = LoopScheduler::Default>
+struct DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle
+    : public DeviceBatchedMultiheadAttentionForward<NumDimG,
+                                                    NumDimM,
+                                                    NumDimN,
+                                                    NumDimK,
+                                                    NumDimO,
+                                                    ADataType,
+                                                    BDataType,
+                                                    B1DataType,
+                                                    CDataType,
+                                                    ZDataType,
+                                                    LSEDataType,
+                                                    Acc0BiasDataType,
+                                                    Acc1BiasDataType,
+                                                    AElementwiseOperation,
+                                                    BElementwiseOperation,
+                                                    AccElementwiseOperation,
+                                                    B1ElementwiseOperation,
+                                                    CElementwiseOperation,
+                                                    MaskingSpec>
+{
+    static_assert(NumDimG > 0 && NumDimM > 0 && NumDimN > 0 && NumDimK > 0 && NumDimO > 0,
+                  "Number of dimension must be greater than 0");
+
+    static constexpr index_t NumAcc0Bias = Acc0BiasDataType::Size();
+    static constexpr index_t NumAcc1Bias = Acc1BiasDataType::Size();
+
+    // TODO ANT: implement bias combination
+    static_assert(NumAcc0Bias == 0 && NumAcc0Bias == 0, "Bias addition is unimplemented");
+
+#if 0
+    // TODO ANT: use alias
+    static constexpr index_t NumDimGemm0M = NumDimM;
+    static constexpr index_t NumDimGemm0N = NumDimN;
+    static constexpr index_t NumDimGemm0K = NumDimK;
+    static constexpr index_t NumDimGemm1M = NumDimM;
+    static constexpr index_t NumDimGemm1N = NumDimO;
+    static constexpr index_t NumDimGemm1K = NumDimN;
+#endif
+
+    using DeviceOp = DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+
+    using Transform = TransformBatchedContractionContractionToBatchedGemmGemm<
+        Sequence<NumDimG, NumDimM, NumDimN, NumDimK, NumDimO>,
+        Sequence<MPerBlock, NPerBlock, KPerBlock, Gemm1NPerBlock>,
+        GemmSpec,
+        ASpec,
+        BSpec,
+        B1Spec,
+        CSpec>;
+
+    static auto MakeAGridDescriptor_AK0_M_AK1(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
+                                              const std::vector<index_t>& a_gs_ms_ks_strides_vec)
+    {
+        return Transform::MakeAGridDescriptor_AK0_M_AK1(
+            Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec),
+            Number<AK1>{});
+    }
+
+    static auto MakeBGridDescriptor_BK0_N_BK1(const std::vector<index_t>& b_gs_ns_ks_lengths_vec,
+                                              const std::vector<index_t>& b_gs_ns_ks_strides_vec)
+    {
+        return Transform::MakeB0GridDescriptor_BK0_N_BK1(
+            Transform::MakeB0GridDescriptor_N_K(b_gs_ns_ks_lengths_vec, b_gs_ns_ks_strides_vec),
+            Number<BK1>{});
+    }
+
+    static auto
+    MakeB1GridDescriptor_BK0_N_BK1(const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths_vec,
+                                   const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides_vec)
+    {
+        return Transform::MakeB1GridDescriptor_BK0_N_BK1(
+            Transform::MakeB1GridDescriptor_N_K(b1_gs_gemm1ns_gemm1ks_lengths_vec,
+                                                b1_gs_gemm1ns_gemm1ks_strides_vec),
+            Number<B1K1>{});
+    }
+
+    static auto MakeZGridDescriptor_M_N(const std::vector<index_t>& z_gs_ms_ns_lengths_vec,
+                                        const std::vector<index_t>& z_gs_ms_ns_strides_vec)
+    {
+        return Transform::MakeCGridDescriptor_M_N(z_gs_ms_ns_lengths_vec, z_gs_ms_ns_strides_vec);
+    }
+
+    static auto MakeLSEGridDescriptor_M(index_t MRaw)
+    {
+        const auto lse_grid_desc_mraw = make_naive_tensor_descriptor_packed(make_tuple(MRaw));
+
+        const auto M    = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
+        const auto MPad = M - MRaw;
+
+        if constexpr(GemmSpec == GemmSpecialization::MPadding ||
+                     GemmSpec == GemmSpecialization::MNPadding ||
+                     GemmSpec == GemmSpecialization::MKPadding ||
+                     GemmSpec == GemmSpecialization::MNKPadding)
+        {
+            // pad M
+            return transform_tensor_descriptor(lse_grid_desc_mraw,
+                                               make_tuple(make_right_pad_transform(MRaw, MPad)),
+                                               make_tuple(Sequence<0>{}),
+                                               make_tuple(Sequence<0>{}));
+        }
+        else
+        {
+            // not pad M
+            return lse_grid_desc_mraw;
+        }
+    }
+
+    using AGridDesc_AK0_M_AK1  = decltype(MakeAGridDescriptor_AK0_M_AK1({}, {}));
+    using BGridDesc_BK0_N_BK1  = decltype(MakeBGridDescriptor_BK0_N_BK1({}, {}));
+    using B1GridDesc_BK0_N_BK1 = decltype(MakeB1GridDescriptor_BK0_N_BK1({}, {}));
+    using CGridDesc_M_N        = decltype(Transform::MakeCGridDescriptor_M_N({}, {}));
+    using ZGridDesc_M_N        = decltype(MakeZGridDescriptor_M_N({}, {}));
+    using LSEGridDesc_M        = decltype(MakeLSEGridDescriptor_M(1));
+    using AGridDesc_G_M_K      = decltype(Transform::MakeAGridDescriptor_G_M_K({}, {}));
+    using BGridDesc_G_N_K      = decltype(Transform::MakeB0GridDescriptor_G_N_K({}, {}));
+    using B1GridDesc_G_N_K     = decltype(Transform::MakeB1GridDescriptor_G_N_K({}, {}));
+    using CGridDesc_G_M_N      = decltype(Transform::MakeCGridDescriptor_G_M_N({}, {}));
+    using ZGridDesc_G_M_N      = decltype(Transform::MakeCGridDescriptor_G_M_N({}, {}));
+
+    constexpr static auto make_MaskOutPredicate()
+    {
+        if constexpr(MaskingSpec == MaskingSpecialization::MaskDisabled)
+        {
+            return MaskDisabledPredicate{};
+        }
+        else if constexpr(MaskingSpec == MaskingSpecialization::MaskOutUpperTriangle)
+        {
+            return MaskOutUpperTrianglePredicate{};
+        }
+    }
+    using C0MatrixMask = C0MatrixMask_impl<decltype(make_MaskOutPredicate())>;
+
+    struct ComputeBasePtrOfStridedBatch
+    {
+        ComputeBasePtrOfStridedBatch(const AGridDesc_G_M_K& a_grid_desc_g_m_k,
+                                     const BGridDesc_G_N_K& b_grid_desc_g_n_k,
+                                     const B1GridDesc_G_N_K& b1_grid_desc_g_n_k,
+                                     const CGridDesc_G_M_N& c_grid_desc_g_m_n,
+                                     const ZGridDesc_G_M_N& z_grid_desc_g_m_n,
+                                     index_t BatchStrideLSE)
+            : a_grid_desc_g_m_k_(a_grid_desc_g_m_k),
+              b_grid_desc_g_n_k_(b_grid_desc_g_n_k),
+              b1_grid_desc_g_n_k_(b1_grid_desc_g_n_k),
+              c_grid_desc_g_m_n_(c_grid_desc_g_m_n),
+              z_grid_desc_g_m_n_(z_grid_desc_g_m_n),
+              BatchStrideLSE_(BatchStrideLSE)
+        {
+        }
+
+        __host__ __device__ constexpr long_index_t GetABasePtr(index_t g_idx) const
+        {
+            return a_grid_desc_g_m_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetBBasePtr(index_t g_idx) const
+        {
+            return b_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetB1BasePtr(index_t g_idx) const
+        {
+            return b1_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetCBasePtr(index_t g_idx) const
+        {
+            return c_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetZBasePtr(index_t g_idx) const
+        {
+            return z_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetLSEBasePtr(index_t g_idx) const
+        {
+            return g_idx * static_cast<long_index_t>(BatchStrideLSE_);
+        }
+
+        private:
+        AGridDesc_G_M_K a_grid_desc_g_m_k_;
+        BGridDesc_G_N_K b_grid_desc_g_n_k_;
+        B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
+        CGridDesc_G_M_N c_grid_desc_g_m_n_;
+        ZGridDesc_G_M_N z_grid_desc_g_m_n_;
+        index_t BatchStrideLSE_;
+    };
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseBatchedMultiheadAttentionForward_Xdl_CShuffle<
+        ADataType, // TODO: distinguish A/B datatype
+        ZDataType,
+        GemmDataType,
+        GemmAccDataType,
+        CShuffleDataType,
+        CDataType,
+        LSEDataType,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        AccElementwiseOperation,
+        B1ElementwiseOperation,
+        CElementwiseOperation,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_AK0_M_AK1,
+        BGridDesc_BK0_N_BK1,
+        B1GridDesc_BK0_N_BK1,
+        CGridDesc_M_N,
+        ZGridDesc_M_N,
+        LSEGridDesc_M,
+        NumGemmKPrefetchStage,
+        BlockSize,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        Gemm1NPerBlock,
+        Gemm1KPerBlock,
+        AK1,
+        BK1,
+        B1K1,
+        MPerXDL,
+        NPerXDL,
+        MXdlPerWave,
+        NXdlPerWave,
+        Gemm1NXdlPerWave,
+        ABlockTransferThreadClusterLengths_AK0_M_AK1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_AK1,
+        true,
+        ABlockLdsExtraM,
+        BBlockTransferThreadClusterLengths_BK0_N_BK1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_BK1,
+        true,
+        BBlockLdsExtraN,
+        B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+        B1BlockTransferThreadClusterArrangeOrder,
+        B1BlockTransferSrcAccessOrder,
+        B1BlockTransferSrcVectorDim,
+        B1BlockTransferSrcScalarPerVector,
+        B1BlockTransferDstScalarPerVector_BK1,
+        false,
+        B1BlockLdsExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        LoopSched,
+        Transform::matrix_padder.PadN,
+        MaskingSpec == MaskingSpecialization::MaskOutUpperTriangle,
+        Deterministic>;
+
+    // Argument
+    // FIXME: constness
+    struct Argument : public BaseArgument
+    {
+        Argument(
+            const ADataType* p_a_grid,
+            const BDataType* p_b_grid,
+            const B1DataType* p_b1_grid,
+            CDataType* p_c_grid,
+            ZDataType* p_z_grid,
+            LSEDataType* p_lse_grid,
+            const std::array<void*, NumAcc0Bias> p_acc0_biases,
+            const std::array<void*, NumAcc1Bias> p_acc1_biases,
+            const std::vector<index_t>& a_gs_ms_ks_lengths,
+            const std::vector<index_t>& a_gs_ms_ks_strides,
+            const std::vector<index_t>& b_gs_ns_ks_lengths,
+            const std::vector<index_t>& b_gs_ns_ks_strides,
+            const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+            const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+            const std::vector<index_t>& c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+            const std::vector<index_t>& c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+            const std::vector<index_t>& z_gs_ms_ns_lengths,
+            const std::vector<index_t>& z_gs_ms_ns_strides,
+            const std::vector<index_t>& lse_gs_ms_lengths,
+            const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ns_lengths,
+            const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ns_strides,
+            const std::array<std::vector<ck::index_t>, NumAcc1Bias>
+                acc1_biases_gs_ms_gemm1ns_lengths, // acc1_biases_gs_ms_os_lengths
+            const std::array<std::vector<ck::index_t>, NumAcc1Bias>
+                acc1_biases_gs_ms_gemm1ns_strides, // acc1_biases_gs_ms_os_strides
+            AElementwiseOperation a_element_op,
+            BElementwiseOperation b_element_op,
+            AccElementwiseOperation acc_element_op,
+            B1ElementwiseOperation b1_element_op,
+            CElementwiseOperation c_element_op,
+            float p_dropout,
+            std::tuple<unsigned long long, unsigned long long> seeds)
+            : p_a_grid_{p_a_grid},
+              p_b_grid_{p_b_grid},
+              p_b1_grid_{p_b1_grid},
+              p_c_grid_{p_c_grid},
+              p_z_grid_{p_z_grid},
+              p_lse_grid_{p_lse_grid},
+              a_grid_desc_ak0_m_ak1_{
+                  DeviceOp::MakeAGridDescriptor_AK0_M_AK1(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
+              b_grid_desc_bk0_n_bk1_{
+                  DeviceOp::MakeBGridDescriptor_BK0_N_BK1(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
+              b1_grid_desc_bk0_n_bk1_{DeviceOp::MakeB1GridDescriptor_BK0_N_BK1(
+                  b1_gs_gemm1ns_gemm1ks_lengths, b1_gs_gemm1ns_gemm1ks_strides)},
+              c_grid_desc_m_n_{Transform::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
+                                                                  c_gs_ms_gemm1ns_strides)},
+              z_grid_desc_m_n_{MakeZGridDescriptor_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
+              lse_grid_desc_m_{DeviceOp::MakeLSEGridDescriptor_M(lse_gs_ms_lengths[NumDimG])},
+              a_grid_desc_g_m_k_{
+                  Transform::MakeAGridDescriptor_G_M_K(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
+              b_grid_desc_g_n_k_{
+                  Transform::MakeB0GridDescriptor_G_N_K(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
+              b1_grid_desc_g_n_k_{Transform::MakeB1GridDescriptor_G_N_K(
+                  b1_gs_gemm1ns_gemm1ks_lengths, b1_gs_gemm1ns_gemm1ks_strides)},
+              c_grid_desc_g_m_n_{Transform::MakeCGridDescriptor_G_M_N(c_gs_ms_gemm1ns_lengths,
+                                                                      c_gs_ms_gemm1ns_strides)},
+              z_grid_desc_g_m_n_{
+                  Transform::MakeCGridDescriptor_G_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
+              c_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_)},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              acc_element_op_{acc_element_op},
+              b1_element_op_{b1_element_op},
+              c_element_op_{c_element_op},
+              c0_matrix_mask_{b_grid_desc_g_n_k_.GetLength(I1)},
+              raw_lengths_mz_nz_kz_gemm1nz_{a_gs_ms_ks_lengths[NumDimG + NumDimM - 1],
+                                            b_gs_ns_ks_lengths[NumDimG + NumDimN - 1],
+                                            b_gs_ns_ks_lengths[NumDimG + NumDimN + NumDimK - 1],
+                                            b1_gs_gemm1ns_gemm1ks_lengths[NumDimG + NumDimO - 1]},
+              a_mz_kz_strides_{a_gs_ms_ks_strides[NumDimG + NumDimM - 1],
+                               a_gs_ms_ks_strides[NumDimG + NumDimM + NumDimK - 1]},
+              b_nz_kz_strides_{b_gs_ns_ks_strides[NumDimG + NumDimN - 1],
+                               b_gs_ns_ks_strides[NumDimG + NumDimN + NumDimK - 1]},
+              b1_nz_kz_strides_{b1_gs_gemm1ns_gemm1ks_strides[NumDimG + NumDimO - 1],
+                                b1_gs_gemm1ns_gemm1ks_strides[NumDimG + NumDimO + NumDimN - 1]},
+              c_mz_gemm1nz_strides_{c_gs_ms_gemm1ns_strides[NumDimG + NumDimM - 1],
+                                    c_gs_ms_gemm1ns_strides[NumDimG + NumDimM + NumDimO - 1]},
+              batch_count_{c_grid_desc_g_m_n_.GetLength(I0)},
+              compute_base_ptr_of_batch_{
+                  a_grid_desc_g_m_k_,
+                  b_grid_desc_g_n_k_,
+                  b1_grid_desc_g_n_k_,
+                  c_grid_desc_g_m_n_,
+                  z_grid_desc_g_m_n_,
+                  type_convert<index_t>(lse_grid_desc_m_.GetElementSpaceSize())}
+        {
+            // TODO ANT: implement bias addition
+            ignore = p_acc0_biases;
+            ignore = p_acc1_biases;
+            ignore = acc0_biases_gs_ms_ns_lengths;
+            ignore = acc0_biases_gs_ms_ns_strides;
+            ignore = acc1_biases_gs_ms_gemm1ns_lengths;
+            ignore = acc1_biases_gs_ms_gemm1ns_strides;
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
+                                           b_grid_desc_bk0_n_bk1_,
+                                           b1_grid_desc_bk0_n_bk1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        c_grid_desc_m_n_);
+            }
+
+            is_dropout_          = p_dropout > 0.0; //
+            p_dropout_           = 1.f - p_dropout;
+            p_dropout_in_16bits_ = uint16_t(std::floor(p_dropout_ * 65535.0));
+            p_dropout_           = 1.f / p_dropout_;
+            p_dropout_rescale_   = type_convert<GemmAccDataType>(p_dropout_);
+
+            seed_   = std::get<0>(seeds);
+            offset_ = std::get<1>(seeds);
+
+            z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_ =
+                GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(z_grid_desc_m_n_);
+
+            if(p_lse_grid == nullptr)
+            {
+                is_lse_storing_ = false;
+            }
+        }
+
+        void Print() const
+        {
+            std::cout << "a_grid_desc_g_m_k_: " << a_grid_desc_g_m_k_.GetLength(I0) << ", "
+                      << a_grid_desc_g_m_k_.GetLength(I1) << ", "
+                      << a_grid_desc_g_m_k_.GetLength(I2) << '\n';
+            std::cout << "b_grid_desc_g_n_k_: " << b_grid_desc_g_n_k_.GetLength(I0) << ", "
+                      << b_grid_desc_g_n_k_.GetLength(I1) << ", "
+                      << b_grid_desc_g_n_k_.GetLength(I2) << '\n';
+            std::cout << "b1_grid_desc_g_n_k_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
+                      << b1_grid_desc_g_n_k_.GetLength(I1) << ", "
+                      << b1_grid_desc_g_n_k_.GetLength(I2) << '\n';
+            std::cout << "c_grid_desc_g_m_n_: " << c_grid_desc_g_m_n_.GetLength(I0) << ", "
+                      << c_grid_desc_g_m_n_.GetLength(I1) << ", "
+                      << c_grid_desc_g_m_n_.GetLength(I2) << '\n';
+        }
+
+        // pointers
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        const B1DataType* p_b1_grid_;
+        CDataType* p_c_grid_;
+        ZDataType* p_z_grid_;
+        LSEDataType* p_lse_grid_;
+
+        // tensor descriptor
+        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
+        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
+        B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        ZGridDesc_M_N z_grid_desc_m_n_;
+        LSEGridDesc_M lse_grid_desc_m_;
+
+        AGridDesc_G_M_K a_grid_desc_g_m_k_;
+        BGridDesc_G_N_K b_grid_desc_g_n_k_;
+        B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
+        CGridDesc_G_M_N c_grid_desc_g_m_n_;
+        ZGridDesc_G_M_N z_grid_desc_g_m_n_;
+        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+            c_grid_desc_mblock_mperblock_nblock_nperblock_;
+
+        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5
+            z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_;
+
+        // block-to-c-tile map
+        typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
+
+        // element-wise op
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        AccElementwiseOperation acc_element_op_;
+        B1ElementwiseOperation b1_element_op_;
+        CElementwiseOperation c_element_op_;
+
+        // check C0 masking and padding
+        C0MatrixMask c0_matrix_mask_;
+
+        // For robust IsSupportedArgument() check
+        std::vector<index_t> raw_lengths_mz_nz_kz_gemm1nz_;
+        std::vector<index_t> a_mz_kz_strides_;
+        std::vector<index_t> b_nz_kz_strides_;
+        std::vector<index_t> b1_nz_kz_strides_;
+        std::vector<index_t> c_mz_gemm1nz_strides_;
+
+        index_t batch_count_;
+        ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch_;
+
+        float p_dropout_;
+        ushort p_dropout_in_16bits_;
+        GemmAccDataType p_dropout_rescale_;
+        unsigned long long seed_;
+        unsigned long long offset_;
+        bool is_dropout_;
+
+        bool is_lse_storing_ = true;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            if(!DeviceOp::IsSupportedArgument(arg))
+            {
+                throw std::runtime_error("wrong! unsupported argument");
+            }
+
+            const index_t grid_size =
+                (Deterministic ? 1
+                               : arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_)) *
+                arg.batch_count_;
+
+            // Gemm0_K
+            const auto K =
+                arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
+
+            float ave_time = 0;
+
+            auto launch_kernel =
+                [&](auto has_main_k_block_loop_, auto is_dropout_, auto is_lse_storing_) {
+                    const auto kernel = kernel_batched_multiheadattention_forward_xdl_cshuffle<
+                        GridwiseGemm,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        ZDataType,
+                        LSEDataType,
+                        GemmAccDataType,
+                        AElementwiseOperation,
+                        BElementwiseOperation,
+                        AccElementwiseOperation,
+                        B1ElementwiseOperation,
+                        CElementwiseOperation,
+                        DeviceOp::AGridDesc_AK0_M_AK1,
+                        DeviceOp::BGridDesc_BK0_N_BK1,
+                        DeviceOp::B1GridDesc_BK0_N_BK1,
+                        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+                        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5,
+                        DeviceOp::LSEGridDesc_M,
+                        typename GridwiseGemm::DefaultBlock2CTileMap,
+                        ComputeBasePtrOfStridedBatch,
+                        C0MatrixMask,
+                        has_main_k_block_loop_,
+                        is_dropout_,
+                        is_lse_storing_,
+                        Deterministic>;
+
+                    return launch_and_time_kernel(
+                        stream_config,
+                        kernel,
+                        dim3(grid_size),
+                        dim3(BlockSize),
+                        0,
+                        arg.p_a_grid_,
+                        arg.p_b_grid_,
+                        arg.p_b1_grid_,
+                        arg.p_c_grid_,
+                        arg.p_z_grid_,
+                        arg.p_lse_grid_,
+                        arg.a_element_op_,
+                        arg.b_element_op_,
+                        arg.acc_element_op_,
+                        arg.b1_element_op_,
+                        arg.c_element_op_,
+                        arg.a_grid_desc_ak0_m_ak1_,
+                        arg.b_grid_desc_bk0_n_bk1_,
+                        arg.b1_grid_desc_bk0_n_bk1_,
+                        arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                        arg.z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5_,
+                        arg.lse_grid_desc_m_,
+                        arg.block_2_ctile_map_,
+                        arg.batch_count_,
+                        arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_),
+                        arg.compute_base_ptr_of_batch_,
+                        arg.c0_matrix_mask_,
+                        arg.p_dropout_in_16bits_,
+                        arg.p_dropout_rescale_,
+                        arg.seed_,
+                        arg.offset_,
+                        arg.raw_lengths_mz_nz_kz_gemm1nz_[0],
+                        arg.raw_lengths_mz_nz_kz_gemm1nz_[1]);
+                };
+
+            // Gemm1_K is split into Gemm1_K0/K1 where K1 is known at compile time, so we only need
+            // to concern Gemm0's loop
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                if(arg.is_dropout_)
+                {
+                    if(arg.is_lse_storing_)
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, true>{},
+                                                 integral_constant<bool, true>{},
+                                                 integral_constant<bool, true>{});
+                    }
+                    else
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, true>{},
+                                                 integral_constant<bool, true>{},
+                                                 integral_constant<bool, false>{});
+                    }
+                }
+                else
+                {
+                    if(arg.is_lse_storing_)
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, true>{},
+                                                 integral_constant<bool, false>{},
+                                                 integral_constant<bool, true>{});
+                    }
+                    else
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, true>{},
+                                                 integral_constant<bool, false>{},
+                                                 integral_constant<bool, false>{});
+                    }
+                }
+            }
+            else
+            {
+                if(arg.is_dropout_)
+                {
+                    if(arg.is_lse_storing_)
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, false>{},
+                                                 integral_constant<bool, true>{},
+                                                 integral_constant<bool, true>{});
+                    }
+                    else
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, false>{},
+                                                 integral_constant<bool, true>{},
+                                                 integral_constant<bool, false>{});
+                    }
+                }
+                else
+                {
+                    if(arg.is_lse_storing_)
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, false>{},
+                                                 integral_constant<bool, false>{},
+                                                 integral_constant<bool, true>{});
+                    }
+                    else
+                    {
+                        ave_time = launch_kernel(integral_constant<bool, false>{},
+                                                 integral_constant<bool, false>{},
+                                                 integral_constant<bool, false>{});
+                    }
+                }
+            }
+
+            return ave_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+#if DEBUG_LOG
+        arg.Print();
+#endif
+
+        if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
+        {
+            return false;
+        }
+
+        // TODO ANT: Check if tensor specialization & strides mismatch
+
+        // Check if C permute dimension matches GEMM + GEMM shape
+        const index_t c_g       = arg.c_grid_desc_g_m_n_.GetLength(I0); // unpadded
+        const index_t c_m       = arg.c_grid_desc_m_n_.GetLength(I0);
+        const index_t c_gemm1n  = arg.c_grid_desc_m_n_.GetLength(I1);
+        const index_t a_m       = arg.a_grid_desc_ak0_m_ak1_.GetLength(I1);
+        const index_t b1_gemm1n = arg.b1_grid_desc_bk0_n_bk1_.GetLength(I1);
+
+        if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n))
+        {
+            return false;
+        }
+
+        // Note: we need raw lengths since threadwise copy can not handle vector load when part of
+        // vector is out of bounds
+        // Note: need lowest dim in Ms/Ns/Ks/Os, not merged M/N/K/O
+        const auto MzRaw      = arg.raw_lengths_mz_nz_kz_gemm1nz_[0];
+        const auto NzRaw      = arg.raw_lengths_mz_nz_kz_gemm1nz_[1];
+        const auto KzRaw      = arg.raw_lengths_mz_nz_kz_gemm1nz_[2];
+        const auto Gemm1NzRaw = arg.raw_lengths_mz_nz_kz_gemm1nz_[3];
+
+        // Check scalar per vector requirement
+        const auto a_extent_lowest  = ABlockTransferSrcVectorDim == 2 ? KzRaw : MzRaw;
+        const auto b_extent_lowest  = BBlockTransferSrcVectorDim == 2 ? KzRaw : NzRaw;
+        const auto b1_extent_lowest = B1BlockTransferSrcVectorDim == 2 ? NzRaw : Gemm1NzRaw;
+        const auto c_extent_lowest  = Gemm1NzRaw;
+
+        if(!(a_extent_lowest % ABlockTransferSrcScalarPerVector == 0 &&
+             b_extent_lowest % BBlockTransferSrcScalarPerVector == 0 &&
+             b1_extent_lowest % B1BlockTransferSrcScalarPerVector == 0 &&
+             c_extent_lowest % CShuffleBlockTransferScalarPerVector_NPerBlock == 0))
+        {
+            return false;
+        }
+
+        // Check vector load/store requirement
+        const auto a_stride_lowest =
+            ABlockTransferSrcVectorDim == 2 ? arg.a_mz_kz_strides_[1] : arg.a_mz_kz_strides_[0];
+        const auto b_stride_lowest =
+            BBlockTransferSrcVectorDim == 2 ? arg.b_nz_kz_strides_[1] : arg.b_nz_kz_strides_[0];
+        const auto b1_stride_lowest =
+            B1BlockTransferSrcVectorDim == 2 ? arg.b1_nz_kz_strides_[1] : arg.b1_nz_kz_strides_[0];
+        const auto c_stride_lowest =
+            arg.c_mz_gemm1nz_strides_[1]; // cshuffle assumes lowest dim in Gemm1Ns to be contiguous
+
+        if(!(a_stride_lowest == 1 || b_stride_lowest == 1 || b1_stride_lowest == 1 ||
+             c_stride_lowest == 1))
+        {
+            return false;
+        }
+
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.b1_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(
+        const ADataType* p_a,
+        const BDataType* p_b,
+        const B1DataType* p_b1,
+        CDataType* p_c,
+        ZDataType* p_z,
+        LSEDataType* p_lse,
+        const std::array<void*, NumAcc0Bias> p_acc0_biases,
+        const std::array<void*, NumAcc1Bias> p_acc1_biases,
+        const std::vector<index_t>& a_gs_ms_ks_lengths,
+        const std::vector<index_t>& a_gs_ms_ks_strides,
+        const std::vector<index_t>& b_gs_ns_ks_lengths,
+        const std::vector<index_t>& b_gs_ns_ks_strides,
+        const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+        const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+        const std::vector<index_t>& c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+        const std::vector<index_t>& c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+        const std::vector<index_t>& z_gs_ms_ns_lengths,
+        const std::vector<index_t>& z_gs_ms_ns_strides,
+        const std::vector<index_t>& lse_gs_ms_lengths,
+        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ns_lengths,
+        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ns_strides,
+        const std::array<std::vector<ck::index_t>, NumAcc1Bias>
+            acc1_biases_gs_ms_gemm1ns_lengths, // acc1_biases_gs_ms_os_lengths
+        const std::array<std::vector<ck::index_t>, NumAcc1Bias>
+            acc1_biases_gs_ms_gemm1ns_strides, // acc1_biases_gs_ms_os_strides
+        AElementwiseOperation a_element_op,
+        BElementwiseOperation b_element_op,
+        AccElementwiseOperation acc_element_op,
+        B1ElementwiseOperation b1_element_op,
+        CElementwiseOperation c_element_op,
+        float p_dropout,
+        std::tuple<unsigned long long, unsigned long long> seeds)
+    {
+        return Argument{p_a,
+                        p_b,
+                        p_b1,
+                        p_c,
+                        p_z,
+                        p_lse,
+                        p_acc0_biases,
+                        p_acc1_biases,
+                        a_gs_ms_ks_lengths,
+                        a_gs_ms_ks_strides,
+                        b_gs_ns_ks_lengths,
+                        b_gs_ns_ks_strides,
+                        b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+                        b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+                        c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+                        c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+                        z_gs_ms_ns_lengths,
+                        z_gs_ms_ns_strides,
+                        lse_gs_ms_lengths,
+                        acc0_biases_gs_ms_ns_lengths,
+                        acc0_biases_gs_ms_ns_strides,
+                        acc1_biases_gs_ms_gemm1ns_lengths, // acc1_biases_gs_ms_os_lengths
+                        acc1_biases_gs_ms_gemm1ns_strides, // acc1_biases_gs_ms_os_strides
+                        a_element_op,
+                        b_element_op,
+                        acc_element_op,
+                        b1_element_op,
+                        c_element_op,
+                        p_dropout,
+                        seeds};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    // FIXME: constness
+    std::unique_ptr<BaseArgument> MakeArgumentPointer(
+        const void* p_a,
+        const void* p_b,
+        const void* p_b1,
+        void* p_c,
+        void* p_z,
+        void* p_lse,
+        const std::array<void*, NumAcc0Bias> p_acc0_biases,
+        const std::array<void*, NumAcc1Bias> p_acc1_biases,
+        const std::vector<index_t>& a_gs_ms_ks_lengths,
+        const std::vector<index_t>& a_gs_ms_ks_strides,
+        const std::vector<index_t>& b_gs_ns_ks_lengths,
+        const std::vector<index_t>& b_gs_ns_ks_strides,
+        const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+        const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+        const std::vector<index_t>& c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+        const std::vector<index_t>& c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+        const std::vector<index_t>& z_gs_ms_ns_lengths,
+        const std::vector<index_t>& z_gs_ms_ns_strides,
+        const std::vector<index_t>& lse_gs_ms_lengths,
+        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ns_lengths,
+        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ns_strides,
+        const std::array<std::vector<ck::index_t>, NumAcc1Bias>
+            acc1_biases_gs_ms_gemm1ns_lengths, // acc1_biases_gs_ms_os_lengths
+        const std::array<std::vector<ck::index_t>, NumAcc1Bias>
+            acc1_biases_gs_ms_gemm1ns_strides, // acc1_biases_gs_ms_os_strides
+        AElementwiseOperation a_element_op,
+        BElementwiseOperation b_element_op,
+        AccElementwiseOperation acc_element_op,
+        B1ElementwiseOperation b1_element_op,
+        CElementwiseOperation c_element_op,
+        float p_dropout,
+        std::tuple<unsigned long long, unsigned long long> seeds) override
+    {
+        return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
+                                          static_cast<const BDataType*>(p_b),
+                                          static_cast<const B1DataType*>(p_b1),
+                                          static_cast<CDataType*>(p_c),
+                                          static_cast<ZDataType*>(p_z),
+                                          static_cast<LSEDataType*>(p_lse),
+                                          p_acc0_biases, // cast in struct Argument
+                                          p_acc1_biases, // cast in struct Argument
+                                          a_gs_ms_ks_lengths,
+                                          a_gs_ms_ks_strides,
+                                          b_gs_ns_ks_lengths,
+                                          b_gs_ns_ks_strides,
+                                          b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+                                          b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+                                          c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+                                          c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+                                          z_gs_ms_ns_lengths,
+                                          z_gs_ms_ns_strides,
+                                          lse_gs_ms_lengths,
+                                          acc0_biases_gs_ms_ns_lengths,
+                                          acc0_biases_gs_ms_ns_strides,
+                                          acc1_biases_gs_ms_gemm1ns_lengths,
+                                          acc1_biases_gs_ms_gemm1ns_strides,
+                                          a_element_op,
+                                          b_element_op,
+                                          acc_element_op,
+                                          b1_element_op,
+                                          c_element_op,
+                                          p_dropout,
+                                          seeds);
+    }
+
+    // polymorphic
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    // polymorphic
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << AK1 << ", "
+            << BK1 << ", "
+            << MPerBlock << ", "
+            << Gemm1NPerBlock << ", "
+            << Gemm1KPerBlock << ", "
+            << B1K1 << ", "
+            << getGemmSpecializationString(GemmSpec) << ", "
+            << "ASpec" << getTensorSpecializationString(ASpec) << ", "
+            << "B0Spec" << getTensorSpecializationString(BSpec) << ", "
+            << "B1Spec" << getTensorSpecializationString(B1Spec) << ", "
+            << "CSpec" << getTensorSpecializationString(CSpec) << ", "
+            << getMaskingSpecializationString(MaskingSpec) << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_backward_xdl_cshuffle_pt4.hpp

@@ -113,23 +113,24 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
 
     // C desc for source in blockwise copy
     __host__ __device__ static constexpr auto
-    MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(const ZGridDesc_M_N& z_grid_desc_m_n)
+    MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(const ZGridDesc_M_N& z_grid_desc_m_n)
     {
         const auto M = z_grid_desc_m_n.GetLength(I0);
         const auto N = z_grid_desc_m_n.GetLength(I1);
 
         constexpr auto mfma = MfmaSelector<GemmDataType, MPerXdl, NPerXdl>::selected_mfma;
-        constexpr auto N3   = mfma.num_groups_per_blk;
-        constexpr auto N4   = mfma.num_input_blks;
-        constexpr auto N5   = mfma.group_size;
+        constexpr auto M3   = mfma.num_groups_per_blk;
+        constexpr auto M4   = mfma.num_input_blks;
+        constexpr auto M5   = mfma.group_size;
+
         return transform_tensor_descriptor(
             z_grid_desc_m_n,
             make_tuple(make_unmerge_transform(
-                           make_tuple(M / MPerBlock, MXdlPerWave, Gemm0MWaves, MPerXdl)),
+                           make_tuple(M / MPerBlock, MXdlPerWave, Gemm0MWaves, M3, M4, M5)),
                        make_unmerge_transform(
-                           make_tuple(N / NPerBlock, NXdlPerWave, Gemm0NWaves, N3, N4, N5))),
+                           make_tuple(N / NPerBlock, NXdlPerWave, Gemm0NWaves, NPerXdl))),
             make_tuple(Sequence<0>{}, Sequence<1>{}),
-            make_tuple(Sequence<0, 2, 4, 6>{}, Sequence<1, 3, 5, 7, 8, 9>{}));
+            make_tuple(Sequence<0, 2, 4, 6, 7, 8>{}, Sequence<1, 3, 5, 9>{}));
     }
 
     __device__ static auto GetGemm0WaveIdx()
@@ -396,8 +397,8 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
     using DefaultBlock2CTileMap =
         remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(KGridDesc_N_K{}))>;
 
-    using ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5 = remove_cvref_t<decltype(
-        MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(ZGridDesc_M_N{}))>;
+    using ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3 = remove_cvref_t<decltype(
+        MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(ZGridDesc_M_N{}))>;
 
     // Q / K / V / dY
     struct GemmBlockwiseCopy
@@ -1234,8 +1235,8 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
                                const CElementwiseOperation& c_element_op,
                                const QGridDesc_K0_M_K1& q_grid_desc_k0_m_k1,
                                const KGridDesc_K0_N_K1& k_grid_desc_k0_n_k1,
-                               const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5&
-                                   z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                               const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3&
+                                   z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
                                const VGridDesc_O0_N_O1& v_grid_desc_o0_n_o1,
                                const YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock&
                                    y_grid_desc_mblock_mperblock_oblock_operblock,
@@ -1245,6 +1246,9 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
                                const C0MatrixMask& c0_matrix_mask,
                                const float p_drop,
                                ck::philox& ph,
+                               const index_t g_idx,
+                               const index_t MRaw,
+                               const index_t NRaw,
                                const index_t block_idx_n)
     {
         const FloatGemmAcc p_dropout  = type_convert<FloatGemmAcc>(1.0f - p_drop);
@@ -1578,70 +1582,69 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
         // z vgpr copy to global
         //
         // z matrix threadwise desc
-        constexpr auto z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5 =
+
+        constexpr auto z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3 =
             make_naive_tensor_descriptor_packed(make_tuple(I1,   // MBlockId
                                                            I1,   // NBlockID
                                                            m0,   // MRepeat
-                                                           I1,   // NRepeat
+                                                           n0,   // NRepeat
                                                            m1,   // MWaveId
                                                            n1,   // NWaveId
-                                                           m2,   // MPerXdl
-                                                           m3,   // NGroupNum
-                                                           m4,   // NInputNum
-                                                           n2)); // registerNum
+                                                           m2,   // MGroupNum
+                                                           m3,   // MInputNum
+                                                           m4,   // registerNum
+                                                           n2)); // NPerXdl
 
         StaticBuffer<AddressSpaceEnum::Vgpr,
                      unsigned short,
-                     z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5.GetElementSpaceSize(),
+                     z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3.GetElementSpaceSize(),
                      true>
             z_tenor_buffer;
         z_tenor_buffer.Clear();
-        // z matrix global desc
-        /*const auto M = q_grid_desc_k0_m_k1.GetLength(I1);
-        const auto N = k_grid_desc_k0_n_k1.GetLength(I1);
 
-        auto z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5 =
-            MakeZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(M, N);*/
-
-        auto z_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_z_grid, z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5.GetElementSpaceSize());
+        // z matrix global desc
+        // ignore = p_z_tmp_grid;
+        auto z_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>( // tmp buffer for shuffle
+            p_z_grid,
+            z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3.GetElementSpaceSize());
 
         const auto wave_id     = GetGemm0WaveIdx();
         const auto wave_m_n_id = GetGemm0WaveMNIdx(wave_id[I2]); // I2: 0~63
 
-        auto z_thread_copy_vgpr_to_global = ThreadwiseTensorSliceTransfer_v1r3<
-            ushort,
-            ZDataType,
-            decltype(z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5),
-            decltype(z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5),
-            tensor_operation::element_wise::PassThrough,
-            Sequence<I1, // MBlockId
-                     I1, // NBlockID
-                     m0, // MRepeat
-                     I1, // NRepeat
-                     m1, // MWaveId
-                     n1, // NWaveId
-                     m2, // MPerXdl
-                     m3, // NGroupNum
-                     m4, // NInputNum
-                     n2>,
-            Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>,
-            9, // DstVectorDim
-            1, // DstScalarPerVector
-            InMemoryDataOperationEnum::Set,
-            1, // DstScalarStrideInVector
-            true>{z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                  make_multi_index(block_work_idx_n, // MBlockId
-                                   0,                // NBlockId
-                                   0,                // mrepeat
-                                   0,                // nrepeat
-                                   wave_id[I0],      // MWaveId
-                                   wave_id[I1],      // NWaveId
-                                   wave_m_n_id[I1],  // MPerXdl
-                                   0,                // group
-                                   wave_m_n_id[I0],  // NInputIndex
-                                   0),
-                  tensor_operation::element_wise::PassThrough{}};
+        auto z_thread_copy_vgpr_to_global =
+            ThreadwiseTensorSliceTransfer_v1r3<ushort,
+                                               ZDataType,
+                                               decltype(
+                                                   z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3),
+                                               decltype(z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3),
+                                               tensor_operation::element_wise::PassThrough,
+                                               Sequence<I1, // MBlockId
+                                                        I1, // NBlockID
+                                                        m0, // MRepeat
+                                                        n0, // NRepeat
+                                                        m1, // MWaveId
+                                                        n1, // NWaveId
+                                                        m2, // MPerXdl
+                                                        m3, // NGroupNum
+                                                        m4, // NInputNum
+                                                        n2>,
+                                               Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>,
+                                               9, // DstVectorDim
+                                               1, // DstScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1, // DstScalarStrideInVector
+                                               true>{z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                                                     make_multi_index(0,                // MBlockId
+                                                                      block_work_idx_n, // NBlockId
+                                                                      0,                // mrepeat
+                                                                      0,                // nrepeat
+                                                                      wave_id[I0],      // MWaveId
+                                                                      wave_id[I1],      // NWaveId
+                                                                      0,                // MPerXdl
+                                                                      wave_m_n_id[I0],  // group
+                                                                      0, // NInputIndex
+                                                                      wave_m_n_id[I1]),
+                                                     tensor_operation::element_wise::PassThrough{}};
 
         //
         // set up Y dot dY
@@ -1795,6 +1798,11 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
                     lse_grid_desc_mb_m0_m1_m2_m3_m4, make_multi_index(1, 0, 0, 0, 0, 0));
                 y_threadwise_copy.MoveSrcSliceWindow(y_grid_desc_mblock_mperblock_oblock_operblock,
                                                      make_multi_index(1, 0, 0, 0));
+
+                z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                    z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                    make_multi_index(1, 0, 0, 0, 0, 0, 0, 0, 0, 0));
+
                 continue;
             }
 
@@ -1950,35 +1958,167 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
             // save z to global
             if(p_z_grid)
             {
-                // P_dropped
-                static_for<0, n0, 1>{}([&](auto i) {
-                    blockwise_dropout.template ApplyDropout<decltype(s_slash_p_thread_buf),
-                                                            decltype(z_tenor_buffer),
-                                                            true,
-                                                            decltype(n0),
-                                                            decltype(i)>(
-                        s_slash_p_thread_buf, ph, z_tenor_buffer);
-
-                    z_thread_copy_vgpr_to_global.Run(
-                        z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                        make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
-                        z_tenor_buffer,
-                        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                        z_grid_buf);
-                    z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                        make_multi_index(0, 0, 0, 1, 0, 0, 0, 0, 0, 0));
-                });
-                z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                    z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                    make_multi_index(0, 0, 0, -n0.value, 0, 0, 0, 0, 0, 0));
+                // 8d thread_desc in thread scope
+                constexpr auto c_thread_lengths =
+                    s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                // 8d block_desc in block scope
+                constexpr auto c_block_lengths =
+                    s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                constexpr auto M0 = c_block_lengths[I0];
+                constexpr auto N0 = c_block_lengths[I1];
+                constexpr auto M1 = c_block_lengths[I2];
+                constexpr auto N1 = c_block_lengths[I3];
+                constexpr auto M2 = c_block_lengths[I4];
+                constexpr auto M3 = c_block_lengths[I5];
+                constexpr auto M4 = c_block_lengths[I6];
+                constexpr auto N2 = c_block_lengths[I7];
+
+                // works like multi-dimension static_for (static_ford), but provides both the linear
+                // index as well as n-d index
+                using Acc0TileIterator = SpaceFillingCurve<
+                    decltype(c_thread_lengths),
+                    typename arithmetic_sequence_gen<0, c_thread_lengths.Size(), 1>::type,
+                    typename uniform_sequence_gen<c_thread_lengths.Size(), 1>::type,
+                    false>; // SnakeCurved
+
+                constexpr auto block_idx_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+                    make_tuple(make_unmerge_transform(make_tuple(M0, M1, M2, M3, M4)),
+                               make_unmerge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                    make_tuple(Sequence<0, 2, 4, 5, 6>{}, Sequence<1, 3, 7>{}));
+
+                // if(get_thread_global_1d_id()==0){
+                //    printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                //}
+                auto acc0_thread_idx = Acc0TileIterator::GetIndex(I0) + acc0_thread_origin;
+                auto m_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                auto n_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                auto m_global = m_local + m_block_data_idx_on_grid;
+                auto n_global = n_local + n_block_data_idx_on_grid;
+
+                // if(get_thread_global_1d_id()==0){
+                //     printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+                // if(get_thread_global_1d_id()==32){
+                //     printf("tid 32 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+
+                auto global_elem_id_raw =
+                    MRaw * NRaw * g_idx + m_global * NRaw + n_global; // unique element global 1d id
+
+                auto global_elem_id =
+                    (global_elem_id_raw % 4) * MRaw + int(global_elem_id_raw / 4) * 4;
+
+                // if(get_block_1d_id() == 0 && get_thread_local_1d_id()==64){
+                //    printf("global_elem_id is %d \n", global_elem_id);
+                //}
+
+                // index_t id_step = Acc0TileIterator::GetNumOfAccess() / n0.value;
+
+                // if(get_thread_global_1d_id() == 0){
+                //    printf("Acc0TileIterator::GetNumOfAccess() is %d \n",
+                //    Acc0TileIterator::GetNumOfAccess()); printf("n0.value is %d \n", n0.value);
+                //    printf("id_step is %d \n", id_step);
+                //}
+
+                // dropout
+                // z_tenor_buffer_tmp -> z_grid_buf_tmp -> shuffle -> z_tenor_buffer -> z_grid_buf
+
+                blockwise_dropout.template ApplyDropoutAttnBwdSaveZ<decltype(s_slash_p_thread_buf),
+                                                                    decltype(z_tenor_buffer),
+                                                                    true>(
+                    s_slash_p_thread_buf, ph, global_elem_id, z_tenor_buffer, MRaw);
+
+                z_thread_copy_vgpr_to_global.Run(z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                                                 make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
+                                                 z_tenor_buffer,
+                                                 z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                                                 z_grid_buf);
+
+                //// P_dropped
+                // static_for<0, n0, 1>{}([&](auto i) {
+                //    blockwise_dropout.template ApplyDropout<decltype(s_slash_p_thread_buf),
+                //                                            decltype(z_tenor_buffer),
+                //                                            true,
+                //                                            decltype(n0),
+                //                                            decltype(i)>(
+                //        s_slash_p_thread_buf, ph, z_tenor_buffer);
+                //
+                //    z_thread_copy_vgpr_to_global.Run(
+                //        z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                //        make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
+                //        z_tenor_buffer,
+                //        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                //        z_grid_buf);
+                //    z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                //        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                //        make_multi_index(0, 0, 0, 1, 0, 0, 0, 0, 0, 0));
+                //});
+                // z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                //    z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                //    make_multi_index(0, 0, 0, -n0.value, 0, 0, 0, 0, 0, 0));
             }
             else
             {
                 ignore = z_grid_buf;
+                // 8d thread_desc in thread scope
+                constexpr auto c_thread_lengths =
+                    s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                // 8d block_desc in block scope
+                constexpr auto c_block_lengths =
+                    s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                constexpr auto M0 = c_block_lengths[I0];
+                constexpr auto N0 = c_block_lengths[I1];
+                constexpr auto M1 = c_block_lengths[I2];
+                constexpr auto N1 = c_block_lengths[I3];
+                constexpr auto M2 = c_block_lengths[I4];
+                constexpr auto M3 = c_block_lengths[I5];
+                constexpr auto M4 = c_block_lengths[I6];
+                constexpr auto N2 = c_block_lengths[I7];
+
+                // works like multi-dimension static_for (static_ford), but provides both the linear
+                // index as well as n-d index
+                using Acc0TileIterator = SpaceFillingCurve<
+                    decltype(c_thread_lengths),
+                    typename arithmetic_sequence_gen<0, c_thread_lengths.Size(), 1>::type,
+                    typename uniform_sequence_gen<c_thread_lengths.Size(), 1>::type,
+                    false>; // SnakeCurved
+
+                constexpr auto block_idx_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+                    make_tuple(make_unmerge_transform(make_tuple(M0, M1, M2, M3, M4)),
+                               make_unmerge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                    make_tuple(Sequence<0, 2, 4, 5, 6>{}, Sequence<1, 3, 7>{}));
+
+                // if(get_thread_global_1d_id()==0){
+                //    printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                //}
+                auto acc0_thread_idx = Acc0TileIterator::GetIndex(I0) + acc0_thread_origin;
+                auto m_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                auto n_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                auto m_global = m_local + m_block_data_idx_on_grid;
+                auto n_global = n_local + n_block_data_idx_on_grid;
+
+                // if(get_thread_global_1d_id()==0){
+                //     printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+                // if(get_thread_global_1d_id()==32){
+                //     printf("tid 32 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+
+                auto global_elem_id_raw =
+                    MRaw * NRaw * g_idx + m_global * NRaw + n_global; // unique element global 1d id
+
+                auto global_elem_id =
+                    (global_elem_id_raw % 4) * MRaw + int(global_elem_id_raw / 4) * 4;
                 // P_dropped
-                blockwise_dropout.template ApplyDropout<decltype(s_slash_p_thread_buf), true>(
-                    s_slash_p_thread_buf, ph);
+                blockwise_dropout
+                    .template ApplyDropoutAttnBwd<decltype(s_slash_p_thread_buf), true>(
+                        s_slash_p_thread_buf, ph, global_elem_id, MRaw);
             }
 
             block_sync_lds(); // wait for gemm1 LDS read
@@ -2161,8 +2301,8 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V1
             qgrad_thread_copy_vgpr_to_global.MoveDstSliceWindow(
                 qgrad_grid_desc_m0_o0_m1_o1_m2_o2_o3_o4, Gemm2::c_block_slice_copy_step); // step M
             z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                make_multi_index(0, 1, 0, 0, 0, 0, 0, 0, 0, 0));
+                z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                make_multi_index(1, 0, 0, 0, 0, 0, 0, 0, 0, 0));
             lse_thread_copy_global_to_vgpr.MoveSrcSliceWindow(lse_grid_desc_mb_m0_m1_m2_m3_m4,
                                                               make_multi_index(1, 0, 0, 0, 0, 0));
             y_threadwise_copy.MoveSrcSliceWindow(y_grid_desc_mblock_mperblock_oblock_operblock,

include/ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_backward_xdl_cshuffle_pt5.hpp

@@ -127,24 +127,26 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
 
     // C desc for source in blockwise copy
     __host__ __device__ static constexpr auto
-    MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(const ZGridDesc_M_N& z_grid_desc_m_n)
+    MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(const ZGridDesc_M_N& z_grid_desc_m_n)
     {
         const auto M = z_grid_desc_m_n.GetLength(I0);
         const auto N = z_grid_desc_m_n.GetLength(I1);
 
         constexpr auto mfma = MfmaSelector<GemmDataType, MPerXdl, NPerXdl>::selected_mfma;
-        constexpr auto N3   = mfma.num_groups_per_blk;
-        constexpr auto N4   = mfma.num_input_blks;
-        constexpr auto N5   = mfma.group_size;
+        constexpr auto M3   = mfma.num_groups_per_blk;
+        constexpr auto M4   = mfma.num_input_blks;
+        constexpr auto M5   = mfma.group_size;
+
         return transform_tensor_descriptor(
             z_grid_desc_m_n,
             make_tuple(make_unmerge_transform(
-                           make_tuple(M / MPerBlock, MXdlPerWave, Gemm0MWaves, MPerXdl)),
+                           make_tuple(M / MPerBlock, MXdlPerWave, Gemm0MWaves, M3, M4, M5)),
                        make_unmerge_transform(
-                           make_tuple(N / NPerBlock, NXdlPerWave, Gemm0NWaves, N3, N4, N5))),
+                           make_tuple(N / NPerBlock, NXdlPerWave, Gemm0NWaves, NPerXdl))),
             make_tuple(Sequence<0>{}, Sequence<1>{}),
-            make_tuple(Sequence<0, 2, 4, 6>{}, Sequence<1, 3, 5, 7, 8, 9>{}));
+            make_tuple(Sequence<0, 2, 4, 6, 7, 8>{}, Sequence<1, 3, 5, 9>{}));
     }
+
     __host__ __device__ static constexpr auto
     MakeZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(const index_t M, const index_t N)
     {
@@ -467,8 +469,8 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
     using DefaultBlock2CTileMap =
         remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(KGridDesc_N_K{}))>;
 
-    using ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5 = remove_cvref_t<decltype(
-        MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(ZGridDesc_M_N{}))>;
+    using ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3 = remove_cvref_t<decltype(
+        MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(ZGridDesc_M_N{}))>;
 
     // S / dP Gemm (type 1 rcc)
     struct Gemm0
@@ -1183,8 +1185,8 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
                                const CElementwiseOperation& c_element_op,
                                const QGridDesc_K0_M_K1& q_grid_desc_k0_m_k1,
                                const KGridDesc_K0_N_K1& k_grid_desc_k0_n_k1,
-                               const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5&
-                                   z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                               const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3&
+                                   z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
                                const VGridDesc_N0_O_N1& v_grid_desc_n0_o_n1,
                                const YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock&
                                    y_grid_desc_mblock_mperblock_oblock_operblock,
@@ -1194,6 +1196,9 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
                                const C0MatrixMask& c0_matrix_mask,
                                const float p_drop,
                                ck::philox& ph,
+                               const index_t g_idx,
+                               const index_t MRaw,
+                               const index_t NRaw,
                                const index_t block_idx_n)
     {
         const FloatGemmAcc p_dropout  = type_convert<FloatGemmAcc>(1.0f - p_drop);
@@ -1558,70 +1563,68 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
         // z vgpr copy to global
         //
         // z matrix threadwise desc
-        constexpr auto z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5 =
+        constexpr auto z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3 =
             make_naive_tensor_descriptor_packed(make_tuple(I1,   // MBlockId
                                                            I1,   // NBlockID
                                                            m0,   // MRepeat
-                                                           I1,   // NRepeat
+                                                           n0,   // NRepeat
                                                            m1,   // MWaveId
                                                            n1,   // NWaveId
-                                                           m2,   // MPerXdl
-                                                           m3,   // NGroupNum
-                                                           m4,   // NInputNum
-                                                           n2)); // registerNum
+                                                           m2,   // MGroupNum
+                                                           m3,   // MInputNum
+                                                           m4,   // registerNum
+                                                           n2)); // NPerXdl
 
         StaticBuffer<AddressSpaceEnum::Vgpr,
                      unsigned short,
-                     z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5.GetElementSpaceSize(),
+                     z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3.GetElementSpaceSize(),
                      true>
             z_tenor_buffer;
         z_tenor_buffer.Clear();
-        // z matrix global desc
-        /*const auto M = q_grid_desc_k0_m_k1.GetLength(I1);
-        const auto N = k_grid_desc_k0_n_k1.GetLength(I1);
-
-        auto z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5 =
-            MakeZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(M, N);*/
 
-        auto z_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-            p_z_grid, z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5.GetElementSpaceSize());
+        // z matrix global desc
+        // ignore = p_z_tmp_grid;
+        auto z_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>( // tmp buffer for shuffle
+            p_z_grid,
+            z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3.GetElementSpaceSize());
 
         const auto wave_id     = GetGemm0WaveIdx();
         const auto wave_m_n_id = GetGemm0WaveMNIdx(wave_id[I2]); // I2: 0~63
 
-        auto z_thread_copy_vgpr_to_global = ThreadwiseTensorSliceTransfer_v1r3<
-            ushort,
-            ZDataType,
-            decltype(z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5),
-            decltype(z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5),
-            tensor_operation::element_wise::PassThrough,
-            Sequence<I1, // MBlockId
-                     I1, // NBlockID
-                     m0, // MRepeat
-                     I1, // NRepeat
-                     m1, // MWaveId
-                     n1, // NWaveId
-                     m2, // MPerXdl
-                     m3, // NGroupNum
-                     m4, // NInputNum
-                     n2>,
-            Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>,
-            9, // DstVectorDim
-            1, // DstScalarPerVector
-            InMemoryDataOperationEnum::Set,
-            1, // DstScalarStrideInVector
-            true>{z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                  make_multi_index(block_work_idx_n, // MBlockId
-                                   0,                // NBlockId
-                                   0,                // mrepeat
-                                   0,                // nrepeat
-                                   wave_id[I0],      // MWaveId
-                                   wave_id[I1],      // NWaveId
-                                   wave_m_n_id[I1],  // MPerXdl
-                                   0,                // group
-                                   wave_m_n_id[I0],  // NInputIndex
-                                   0),
-                  tensor_operation::element_wise::PassThrough{}};
+        auto z_thread_copy_vgpr_to_global =
+            ThreadwiseTensorSliceTransfer_v1r3<ushort,
+                                               ZDataType,
+                                               decltype(
+                                                   z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3),
+                                               decltype(z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3),
+                                               tensor_operation::element_wise::PassThrough,
+                                               Sequence<I1, // MBlockId
+                                                        I1, // NBlockID
+                                                        m0, // MRepeat
+                                                        n0, // NRepeat
+                                                        m1, // MWaveId
+                                                        n1, // NWaveId
+                                                        m2, // MPerXdl
+                                                        m3, // NGroupNum
+                                                        m4, // NInputNum
+                                                        n2>,
+                                               Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>,
+                                               9, // DstVectorDim
+                                               1, // DstScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1, // DstScalarStrideInVector
+                                               true>{z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                                                     make_multi_index(0,                // MBlockId
+                                                                      block_work_idx_n, // NBlockId
+                                                                      0,                // mrepeat
+                                                                      0,                // nrepeat
+                                                                      wave_id[I0],      // MWaveId
+                                                                      wave_id[I1],      // NWaveId
+                                                                      0,                // MPerXdl
+                                                                      wave_m_n_id[I0],  // group
+                                                                      0, // NInputIndex
+                                                                      wave_m_n_id[I1]),
+                                                     tensor_operation::element_wise::PassThrough{}};
 
         //
         // set up Y dot dY
@@ -1743,8 +1746,8 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
                     y_grid_desc_mblock_mperblock_oblock_operblock, make_multi_index(1, 0, 0, 0));
 
                 z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                    z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                    make_multi_index(0, 1, 0, 0, 0, 0, 0, 0, 0, 0));
+                    z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                    make_multi_index(1, 0, 0, 0, 0, 0, 0, 0, 0, 0));
                 continue;
             }
 
@@ -1891,35 +1894,144 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
             // save z to global
             if(p_z_grid)
             {
-                // P_dropped
-                static_for<0, n0, 1>{}([&](auto i) {
-                    blockwise_dropout.template ApplyDropout<decltype(s_slash_p_thread_buf),
-                                                            decltype(z_tenor_buffer),
-                                                            true,
-                                                            decltype(n0),
-                                                            decltype(i)>(
-                        s_slash_p_thread_buf, ph, z_tenor_buffer);
-
-                    z_thread_copy_vgpr_to_global.Run(
-                        z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                        make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
-                        z_tenor_buffer,
-                        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                        z_grid_buf);
-                    z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                        make_multi_index(0, 0, 0, 1, 0, 0, 0, 0, 0, 0));
-                });
-                z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                    z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                    make_multi_index(0, 0, 0, -n0.value, 0, 0, 0, 0, 0, 0));
+                // 8d thread_desc in thread scope
+                constexpr auto c_thread_lengths =
+                    s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                // 8d block_desc in block scope
+                constexpr auto c_block_lengths =
+                    s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                constexpr auto M0 = c_block_lengths[I0];
+                constexpr auto N0 = c_block_lengths[I1];
+                constexpr auto M1 = c_block_lengths[I2];
+                constexpr auto N1 = c_block_lengths[I3];
+                constexpr auto M2 = c_block_lengths[I4];
+                constexpr auto M3 = c_block_lengths[I5];
+                constexpr auto M4 = c_block_lengths[I6];
+                constexpr auto N2 = c_block_lengths[I7];
+
+                // works like multi-dimension static_for (static_ford), but provides both the linear
+                // index as well as n-d index
+                using Acc0TileIterator = SpaceFillingCurve<
+                    decltype(c_thread_lengths),
+                    typename arithmetic_sequence_gen<0, c_thread_lengths.Size(), 1>::type,
+                    typename uniform_sequence_gen<c_thread_lengths.Size(), 1>::type,
+                    false>; // SnakeCurved
+
+                constexpr auto block_idx_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+                    make_tuple(make_unmerge_transform(make_tuple(M0, M1, M2, M3, M4)),
+                               make_unmerge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                    make_tuple(Sequence<0, 2, 4, 5, 6>{}, Sequence<1, 3, 7>{}));
+
+                // if(get_thread_global_1d_id()==0){
+                //    printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                //}
+                auto acc0_thread_idx = Acc0TileIterator::GetIndex(I0) + acc0_thread_origin;
+                auto m_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                auto n_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                auto m_global = m_local + m_block_data_idx_on_grid;
+                auto n_global = n_local + n_block_data_idx_on_grid;
+
+                // if(get_thread_global_1d_id()==0){
+                //     printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+                // if(get_thread_global_1d_id()==32){
+                //     printf("tid 32 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+
+                auto global_elem_id_raw =
+                    MRaw * NRaw * g_idx + m_global * NRaw + n_global; // unique element global 1d id
+
+                auto global_elem_id =
+                    (global_elem_id_raw % 4) * MRaw + int(global_elem_id_raw / 4) * 4;
+
+                // if(get_block_1d_id() == 0 && get_thread_local_1d_id()==64){
+                //    printf("global_elem_id is %d \n", global_elem_id);
+                //}
+
+                // index_t id_step = Acc0TileIterator::GetNumOfAccess() / n0.value;
+
+                // if(get_thread_global_1d_id() == 0){
+                //    printf("Acc0TileIterator::GetNumOfAccess() is %d \n",
+                //    Acc0TileIterator::GetNumOfAccess()); printf("n0.value is %d \n", n0.value);
+                //    printf("id_step is %d \n", id_step);
+                //}
+
+                // dropout
+                // z_tenor_buffer_tmp -> z_grid_buf_tmp -> shuffle -> z_tenor_buffer -> z_grid_buf
+
+                blockwise_dropout.template ApplyDropoutAttnBwdSaveZ<decltype(s_slash_p_thread_buf),
+                                                                    decltype(z_tenor_buffer),
+                                                                    true>(
+                    s_slash_p_thread_buf, ph, global_elem_id, z_tenor_buffer, MRaw);
+
+                z_thread_copy_vgpr_to_global.Run(z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                                                 make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
+                                                 z_tenor_buffer,
+                                                 z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                                                 z_grid_buf);
             }
             else
             {
                 ignore = z_grid_buf;
+                // 8d thread_desc in thread scope
+                constexpr auto c_thread_lengths =
+                    s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                // 8d block_desc in block scope
+                constexpr auto c_block_lengths =
+                    s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+                constexpr auto M0 = c_block_lengths[I0];
+                constexpr auto N0 = c_block_lengths[I1];
+                constexpr auto M1 = c_block_lengths[I2];
+                constexpr auto N1 = c_block_lengths[I3];
+                constexpr auto M2 = c_block_lengths[I4];
+                constexpr auto M3 = c_block_lengths[I5];
+                constexpr auto M4 = c_block_lengths[I6];
+                constexpr auto N2 = c_block_lengths[I7];
+
+                // works like multi-dimension static_for (static_ford), but provides both the linear
+                // index as well as n-d index
+                using Acc0TileIterator = SpaceFillingCurve<
+                    decltype(c_thread_lengths),
+                    typename arithmetic_sequence_gen<0, c_thread_lengths.Size(), 1>::type,
+                    typename uniform_sequence_gen<c_thread_lengths.Size(), 1>::type,
+                    false>; // SnakeCurved
+
+                constexpr auto block_idx_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+                    make_tuple(make_unmerge_transform(make_tuple(M0, M1, M2, M3, M4)),
+                               make_unmerge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                    make_tuple(Sequence<0, 2, 4, 5, 6>{}, Sequence<1, 3, 7>{}));
+
+                // if(get_thread_global_1d_id()==0){
+                //    printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                //}
+                auto acc0_thread_idx = Acc0TileIterator::GetIndex(I0) + acc0_thread_origin;
+                auto m_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                auto n_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                auto m_global = m_local + m_block_data_idx_on_grid;
+                auto n_global = n_local + n_block_data_idx_on_grid;
+
+                // if(get_thread_global_1d_id()==0){
+                //     printf("tid 0 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+                // if(get_thread_global_1d_id()==32){
+                //     printf("tid 32 m_global & n_global is %d & %d \n", m_global , n_global);
+                // }
+
+                auto global_elem_id_raw =
+                    MRaw * NRaw * g_idx + m_global * NRaw + n_global; // unique element global 1d id
+
+                auto global_elem_id =
+                    (global_elem_id_raw % 4) * MRaw + int(global_elem_id_raw / 4) * 4;
                 // P_dropped
-                blockwise_dropout.template ApplyDropout<decltype(s_slash_p_thread_buf), true>(
-                    s_slash_p_thread_buf, ph);
+                blockwise_dropout
+                    .template ApplyDropoutAttnBwd<decltype(s_slash_p_thread_buf), true>(
+                        s_slash_p_thread_buf, ph, global_elem_id, MRaw);
             }
 
             block_sync_lds(); // wait for gemm1 LDS read
@@ -2183,8 +2295,8 @@ struct GridwiseBatchedMultiheadAttentionBackward_Xdl_CShuffle_V2
                                                       make_multi_index(1, 0, 0, 0));
 
             z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
-                make_multi_index(0, 1, 0, 0, 0, 0, 0, 0, 0, 0));
+                z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                make_multi_index(1, 0, 0, 0, 0, 0, 0, 0, 0, 0));
 
         } while(++gemm0_m_block_outer_index < num_gemm0_m_block_outer_loop); // end j loop
 

include/ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_forward_xdl_cshuffle_pt2.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/utility/philox_rand.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_softmax.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_dropout.hpp"
+
+namespace ck {
+
+template <typename FloatAB,
+          typename ZDataType,
+          typename FloatGemm,
+          typename FloatGemmAcc,
+          typename FloatCShuffle,
+          typename FloatC,
+          typename FloatLSE,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename AccElementwiseOperation,
+          typename B1ElementwiseOperation,
+          typename CElementwiseOperation,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename B1GridDesc_BK0_N_BK1,
+          typename CGridDesc_M_N,
+          typename ZGridDesc_M_N,
+          typename LSEGridDesc_M,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t Gemm1NPerBlock,
+          index_t Gemm1KPerBlock,
+          index_t AK1Value,
+          index_t BK1Value,
+          index_t B1K1Value,
+          index_t MPerXdl,
+          index_t NPerXdl,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          index_t Gemm1NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool AThreadTransferSrcResetCoordinateAfterRun, // ignored
+          index_t ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BThreadTransferSrcResetCoordinateAfterRun, // ignored
+          index_t BBlockLdsExtraN,
+          typename B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename B1BlockTransferThreadClusterArrangeOrder,
+          typename B1BlockTransferSrcAccessOrder,
+          index_t B1BlockTransferSrcVectorDim,
+          index_t B1BlockTransferSrcScalarPerVector,
+          index_t B1BlockTransferDstScalarPerVector_BK1,
+          bool B1ThreadTransferSrcResetCoordinateAfterRun,
+          index_t B1BlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched,
+          bool PadN,
+          bool MaskOutUpperTriangle,
+          bool Deterministic,
+          PipelineVersion PipelineVer = PipelineVersion::v1>
+struct GridwiseBatchedMultiheadAttentionForward_Xdl_CShuffle
+{
+    static_assert(LoopSched == LoopScheduler::Default,
+                  "Non-default loop scheduler is currently not supported");
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    static constexpr auto WaveSize = 64;
+
+    // K1 should be Number<...>
+    // Gemm0
+    static constexpr auto AK0 = Number<KPerBlock / AK1Value>{};
+    static constexpr auto BK0 = Number<KPerBlock / BK1Value>{};
+    static constexpr auto AK1 = Number<AK1Value>{};
+    static constexpr auto BK1 = Number<BK1Value>{};
+
+    static constexpr auto Gemm0MWaves = MPerBlock / (MPerXdl * MXdlPerWave);
+    static constexpr auto Gemm0NWaves = NPerBlock / (NPerXdl * NXdlPerWave);
+
+    // Gemm1
+    static constexpr auto B1K0 = Number<Gemm1KPerBlock / B1K1Value>{};
+    static constexpr auto B1K1 = Number<B1K1Value>{};
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    using GridwiseGemmPipe = remove_cvref_t<decltype(
+        GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
+
+    // C desc for source in gridwise copy
+    __host__ __device__ static constexpr auto MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(
+        const ZGridDesc_M_N& z_grid_desc_m_n) ////=> for z use
+    {
+        const auto M = z_grid_desc_m_n.GetLength(I0);
+        const auto N = z_grid_desc_m_n.GetLength(I1);
+
+        constexpr auto mfma = MfmaSelector<FloatGemm, MPerXdl, NPerXdl>::selected_mfma;
+        constexpr auto N3   = mfma.num_groups_per_blk;
+        constexpr auto N4   = mfma.num_input_blks;
+        constexpr auto N5   = mfma.group_size;
+        return transform_tensor_descriptor(
+            z_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(
+                           make_tuple(M / MPerBlock, MXdlPerWave, Gemm0MWaves, MPerXdl)),
+                       make_unmerge_transform(
+                           make_tuple(N / NPerBlock, NXdlPerWave, Gemm0NWaves, N3, N4, N5))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 2, 4, 6>{}, Sequence<1, 3, 5, 7, 8, 9>{}));
+    }
+
+    __device__ static auto GetGemm0WaveIdx()
+    {
+        const index_t thread_id = get_thread_local_1d_id();
+
+        constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_merge_transform(make_tuple(Gemm0MWaves, Gemm0NWaves, WaveSize))),
+            make_tuple(Sequence<0, 1, 2>{}),
+            make_tuple(Sequence<0>{}));
+
+        return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
+    }
+
+    __device__ static auto GetGemm0WaveMNIdx(const index_t thread_id)
+    {
+        constexpr auto wave_threadid_to_mn_idx_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_merge_transform(make_tuple(WaveSize / MPerXdl, MPerXdl))),
+            make_tuple(Sequence<0, 1>{}),
+            make_tuple(Sequence<0>{}));
+
+        return wave_threadid_to_mn_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
+    }
+
+    template <typename ABlockDesc_AK0_M_AK1>
+    __host__ __device__ static constexpr auto
+    MakeGemm0AMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1&)
+    {
+        constexpr index_t MWaves = MPerBlock / (MXdlPerWave * MPerXdl);
+
+        return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<MXdlPerWave, MWaves, MPerXdl>(
+            ABlockDesc_AK0_M_AK1{});
+    }
+
+    template <typename BBlockDesc_BK0_N_BK1>
+    __host__ __device__ static constexpr auto
+    MakeGemm0BMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1&)
+    {
+        constexpr index_t NWaves = NPerBlock / (NXdlPerWave * NPerXdl);
+
+        return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<NXdlPerWave, NWaves, NPerXdl>(
+            BBlockDesc_BK0_N_BK1{});
+    }
+
+    template <typename ABlockDesc_AK0_M_AK1>
+    __host__ __device__ static constexpr auto
+    MakeGemm1AMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1&)
+    {
+        return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<MXdlPerWave, 1, 1>(ABlockDesc_AK0_M_AK1{});
+    }
+
+    template <typename BBlockDesc_BK0_N_BK1>
+    __host__ __device__ static constexpr auto
+    MakeGemm1BMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1&)
+    {
+        constexpr index_t Gemm1NWaves = Gemm1NPerBlock / (Gemm1NXdlPerWave * NPerXdl);
+        return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<Gemm1NXdlPerWave, Gemm1NWaves, NPerXdl>(
+            BBlockDesc_BK0_N_BK1{});
+    }
+
+    __host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
+    {
+        // A matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(AK0, Number<MPerBlock>{}, AK1),
+            make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
+    }
+
+    __host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
+    {
+        // B matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(BK0, Number<NPerBlock>{}, BK1),
+            make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
+    }
+
+    __host__ __device__ static constexpr auto GetB1BlockDescriptor_BK0PerBlock_NPerBlock_BK1()
+    {
+        // B1 matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(B1K0, Number<Gemm1NPerBlock>{}, B1K1),
+            make_tuple(Number<Gemm1NPerBlock + B1BlockLdsExtraN>{} * B1K1, B1K1, I1));
+    }
+
+    __host__ __device__ static constexpr auto
+    GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
+    {
+        constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+        constexpr index_t NWave = Gemm1NPerBlock / (Gemm1NXdlPerWave * NPerXdl);
+
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            make_naive_tensor_descriptor_packed(
+                make_tuple(I1,
+                           Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
+                           I1,
+                           Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
+
+        return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
+    {
+        const index_t gemm0_bytes_end = (SharedMemTrait::a_block_space_size_aligned +
+                                         SharedMemTrait::b_block_space_size_aligned) *
+                                        sizeof(FloatGemm);
+        const index_t gemm1_bytes_end =
+            (SharedMemTrait::b1_block_space_offset + SharedMemTrait::b1_block_space_size_aligned) *
+            sizeof(FloatGemm);
+        const index_t softmax_bytes_end = (SharedMemTrait::reduction_space_offset +
+                                           SharedMemTrait::reduction_space_size_aligned) *
+                                          sizeof(FloatGemmAcc);
+        const index_t c_block_bytes_end =
+            SharedMemTrait::c_block_space_size * sizeof(FloatCShuffle);
+
+        const index_t z_block_bytes_end =
+            SharedMemTrait::z_shuffle_block_space_size * sizeof(ZDataType);
+
+        return math::max(gemm0_bytes_end,
+                         gemm1_bytes_end,
+                         softmax_bytes_end,
+                         c_block_bytes_end,
+                         z_block_bytes_end);
+    }
+
+    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
+    template <typename Block2CTileMap>
+    __host__ __device__ static constexpr bool
+    CheckValidity(const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
+                  const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
+                  const B1GridDesc_BK0_N_BK1& b1_grid_desc_bk0_n_bk1,
+                  const CGridDesc_M_N& c_grid_desc_m_n,
+                  const Block2CTileMap& block_2_ctile_map)
+    {
+        static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
+                          (NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
+                      "Invalid tuning param!");
+
+        const auto M = a_grid_desc_ak0_m_ak1.GetLength(I1);
+        const auto N = b_grid_desc_bk0_n_bk1.GetLength(I1);
+        const auto K = a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2);
+        const auto Gemm1N = b1_grid_desc_bk0_n_bk1.GetLength(I1);
+
+        // if(Gemm1N != K)
+        // {
+        //     std::cout << "SizeK must be equal to SizeO (equal attention head size)" << '\n';
+        //     return false;
+        // }
+
+        if(!(M == c_grid_desc_m_n.GetLength(I0) && Gemm1N == c_grid_desc_m_n.GetLength(I1)))
+        {
+            return false;
+        }
+
+        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0 &&
+             Gemm1N % Gemm1NPerBlock == 0))
+        {
+            return false;
+        }
+
+        // check gemm0 gridwise gemm pipeline
+        const auto num_gemm0_k_loop = K / KPerBlock;
+        if(!GridwiseGemmPipe::IsSupported(num_gemm0_k_loop))
+        {
+            return false;
+        }
+
+        // check gemm1 gridwise gemm pipeline
+        if(!(NPerBlock % Gemm1KPerBlock == 0))
+        {
+            return false;
+        }
+
+        const auto num_gemm1_k_inner_loop = NPerBlock / Gemm1KPerBlock;
+        if(!GridwiseGemmPipe::IsSupported(num_gemm1_k_inner_loop))
+        {
+            return false;
+        }
+
+        if(!block_2_ctile_map.CheckValidity(c_grid_desc_m_n))
+        {
+            return false;
+        }
+
+        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
+    {
+        const index_t num_loop = K / KPerBlock;
+
+        return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
+    }
+
+    __host__ __device__ static constexpr auto
+    MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const CGridDesc_M_N& c_grid_desc_m_n)
+    {
+        const auto M = c_grid_desc_m_n.GetLength(I0);
+        const auto N = c_grid_desc_m_n.GetLength(I1);
+
+        const auto MBlock = M / MPerBlock;
+        const auto NBlock = N / Gemm1NPerBlock;
+
+        const auto c_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
+            c_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
+                       make_unmerge_transform(make_tuple(NBlock, Number<Gemm1NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+
+        return c_grid_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    __host__ __device__ static constexpr auto
+    MakeLSEGridDescriptor_MBlock_MRepeat_NWave_MPerXdl(const LSEGridDesc_M& lse_grid_desc_m)
+    {
+        const index_t M         = lse_grid_desc_m.GetLength(I0);
+        const index_t MBlock    = M / MPerBlock;
+        constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+
+        const auto lse_grid_desc_mblock_mrepeat_mwave_mperxdl = transform_tensor_descriptor(
+            lse_grid_desc_m,
+            make_tuple(make_unmerge_transform(
+                make_tuple(MBlock, Number<MXdlPerWave>{}, MWave, Number<MPerXdl>{}))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2, 3>{}));
+
+        return lse_grid_desc_mblock_mrepeat_mwave_mperxdl;
+    }
+
+    // return block_id to C matrix tile idx (m0, n0) mapping
+    __host__ __device__ static constexpr auto
+    MakeDefaultBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n)
+    {
+        return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, Gemm1NPerBlock, CGridDesc_M_N>(
+            c_grid_desc_m_n);
+    }
+
+    using CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
+        MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}))>;
+
+    using DefaultBlock2CTileMap =
+        remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}))>;
+
+    using ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5 = remove_cvref_t<decltype(
+        MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5(ZGridDesc_M_N{}))>;
+
+    struct SharedMemTrait
+    {
+        // LDS allocation for A and B: be careful of alignment
+        static constexpr auto a_block_desc_ak0_m_ak1 =
+            GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+        static constexpr auto b_block_desc_bk0_n_bk1 =
+            GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+        static constexpr auto b1_block_desc_bk0_n_bk1 =
+            GetB1BlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        static constexpr auto max_lds_align = math::lcm(math::lcm(AK1, BK1), B1K1);
+
+        static constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+        static constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
+        static constexpr auto b1_block_space_size_aligned = math::integer_least_multiple(
+            b1_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
+
+        static constexpr auto a_block_space_offset  = 0;
+        static constexpr auto b_block_space_offset  = a_block_space_size_aligned.value;
+        static constexpr auto b1_block_space_offset = 0;
+
+        // LDS allocation for reduction
+        static constexpr index_t reduction_space_size_aligned =
+            math::integer_least_multiple(BlockSize, max_lds_align);
+
+        static constexpr auto reduction_space_offset = 0;
+
+        // LDS allocation for C shuffle in LDS
+        static constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+        static constexpr auto c_block_space_size =
+            c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
+
+        static constexpr auto z_shuffle_block_space_size = MPerBlock * NPerBlock;
+    };
+
+    template <bool HasMainKBlockLoop,
+              bool IsDropout,
+              bool IsLseStoring,
+              typename Block2CTileMap,
+              typename C0MatrixMask>
+    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
+                               const FloatAB* __restrict__ p_b_grid,
+                               const FloatAB* __restrict__ p_b1_grid,
+                               FloatC* __restrict__ p_c_grid,
+                               ZDataType* __restrict__ p_z_grid,
+                               FloatLSE* __restrict__ p_lse_grid,
+                               void* __restrict__ p_shared,
+                               const AElementwiseOperation& a_element_op,
+                               const BElementwiseOperation& b_element_op,
+                               const AccElementwiseOperation& acc_element_op,
+                               const B1ElementwiseOperation& b1_element_op,
+                               const CElementwiseOperation& c_element_op,
+                               const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
+                               const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
+                               const B1GridDesc_BK0_N_BK1& b1_grid_desc_bk0_n_bk1,
+                               const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
+                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
+                               const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_N3_N4_N5&
+                                   z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                               const LSEGridDesc_M& lse_grid_desc_m,
+                               const Block2CTileMap& block_2_ctile_map,
+                               const C0MatrixMask& c0_matrix_mask,
+                               const ushort p_dropout_in_16bits,
+                               FloatGemmAcc p_dropout_rescale,
+                               ck::philox& ph,
+                               const index_t g_idx,
+                               const index_t MRaw,
+                               const index_t NRaw,
+                               const index_t block_idx_m)
+    {
+        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
+        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
+        const auto b1_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b1_grid, b1_grid_desc_bk0_n_bk1.GetElementSpaceSize());
+        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+        auto lse_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_lse_grid, lse_grid_desc_m.GetElementSpaceSize());
+
+        // divide block work by [M, N]
+        const auto block_work_idx =
+            block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
+
+        if(!block_2_ctile_map.ValidCTileIndex(
+               block_work_idx,
+               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
+                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
+        {
+            return;
+        }
+
+        const index_t block_work_idx_m = Deterministic ? block_idx_m : block_work_idx[I0];
+
+        // HACK: this force m/gemm1_n_block_data_idx_on_grid into SGPR
+        const index_t m_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx_m * MPerBlock);
+
+        const index_t gemm1_n_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * Gemm1NPerBlock);
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        //
+        // set up Gemm0
+        //
+
+        // A matrix blockwise copy
+        auto a_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                AElementwiseOperation,
+                                                tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<AK0, MPerBlock, AK1>,
+                                                ABlockTransferThreadClusterLengths_AK0_M_AK1,
+                                                ABlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatGemm,
+                                                decltype(a_grid_desc_ak0_m_ak1),
+                                                decltype(a_block_desc_ak0_m_ak1),
+                                                ABlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                ABlockTransferSrcVectorDim,
+                                                2,
+                                                ABlockTransferSrcScalarPerVector,
+                                                ABlockTransferDstScalarPerVector_AK1,
+                                                1,
+                                                1,
+                                                true, // SrcResetCoord
+                                                true, // DstResetCoord
+                                                NumGemmKPrefetchStage>(
+                a_grid_desc_ak0_m_ak1,
+                make_multi_index(0, m_block_data_idx_on_grid, 0),
+                a_element_op,
+                a_block_desc_ak0_m_ak1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        // B matrix blockwise copy
+        auto b_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<BK0, NPerBlock, BK1>,
+                                                BBlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                BBlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatGemm,
+                                                decltype(b_grid_desc_bk0_n_bk1),
+                                                decltype(b_block_desc_bk0_n_bk1),
+                                                BBlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                BBlockTransferSrcVectorDim,
+                                                2,
+                                                BBlockTransferSrcScalarPerVector,
+                                                BBlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                true, // SrcResetCoord
+                                                true, // DstResetCoord
+                                                NumGemmKPrefetchStage>(
+                b_grid_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0), // will loop over GemmN dimension
+                b_element_op,
+                b_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        // Fused Gemm+Gemm pipeline
+        // for n in N0:
+        //   for k in K0:
+        //     acc[m][n] += A[m][k] * B0[k][n]
+        //   acc1[m][o] += acc[m][n] * B1[n][o]
+
+        // sanity check
+        constexpr index_t KPack =
+            math::max(math::lcm(AK1, BK1),
+                      MfmaSelector<FloatGemm, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
+
+        auto blockwise_gemm = BlockwiseGemmXdlops_v2<
+            BlockSize,
+            FloatGemm,
+            FloatGemmAcc,
+            decltype(a_block_desc_ak0_m_ak1),
+            decltype(b_block_desc_bk0_n_bk1),
+            decltype(MakeGemm0AMmaTileDescriptor_M0_M1_M2_K(a_block_desc_ak0_m_ak1)),
+            decltype(MakeGemm0BMmaTileDescriptor_N0_N1_N2_K(b_block_desc_bk0_n_bk1)),
+            MPerBlock,
+            NPerBlock,
+            KPerBlock,
+            MPerXdl,
+            NPerXdl,
+            MXdlPerWave,
+            NXdlPerWave,
+            KPack,
+            true>{}; // TransposeC
+
+        auto acc_thread_buf = blockwise_gemm.GetCThreadBuffer();
+
+        // LDS allocation for A and B: be careful of alignment
+        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatGemm*>(p_shared) + SharedMemTrait::a_block_space_offset,
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatGemm*>(p_shared) + SharedMemTrait::b_block_space_offset,
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
+        constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
+        const auto a_block_reset_copy_step =
+            make_multi_index(-a_grid_desc_ak0_m_ak1.GetLength(I0), 0, 0);
+        const auto b_block_reset_copy_step =
+            make_multi_index(-b_grid_desc_bk0_n_bk1.GetLength(I0), NPerBlock, 0);
+
+        // gridwise GEMM pipeline
+        // Only supports LoopScheduler::Default
+        const auto gridwise_gemm_pipeline = GridwiseGemmPipeline_Selector<PipelineVer,
+                                                                          NumGemmKPrefetchStage,
+                                                                          LoopScheduler::Default>();
+
+        const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
+            (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
+            KPerBlock);
+
+        //
+        // set up Gemm1
+        //
+
+        // Acc matrix threadwise copy: AccVGPR to VGPR and downcast to XDL input data type
+        constexpr auto acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4 =
+            blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+
+        constexpr auto m0 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I0);
+        constexpr auto n0 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I1);
+        constexpr auto m1 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I2);
+        constexpr auto n1 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I3);
+        constexpr auto m2 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I4);
+        constexpr auto n2 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I5);
+        constexpr auto n3 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I6);
+        constexpr auto n4 = acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I7);
+
+        constexpr auto b1_block_slice_copy_step = make_multi_index(Gemm1KPerBlock / B1K1, 0, 0);
+
+        // acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4 to acc_thread_desc_k0_m_k1
+        // n0_n1_n2_n3 -> k0
+        // m0_m1_m2 -> m
+        // n4 -> k1
+        // NOTE: had to use merge_v3 or will spit out compilation errors
+        constexpr auto acc_thread_desc_k0_m_k1 = transform_tensor_descriptor(
+            acc_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+            make_tuple(make_merge_transform_v3_division_mod(make_tuple(n0, n1, n2, n3)),
+                       make_merge_transform_v3_division_mod(make_tuple(m0, m1, m2)),
+                       make_pass_through_transform(n4)),
+            make_tuple(Sequence<1, 3, 5, 6>{}, Sequence<0, 2, 4>{}, Sequence<7>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+        // A1 matrix in AccVGPR
+        // N2 num_groups_per_blk, N3 num_input_blks, N4 group_size
+        constexpr auto AccN3 =
+            blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLength(I6);
+        constexpr auto AccM2 =
+            blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLength(I4);
+
+        constexpr auto A1ThreadSlice_K0_M_K1 =
+            make_tuple(Number<Gemm1KPerBlock / n4 / AccN3>{}, Number<m0 * m1 * m2>{}, Number<n4>{});
+
+        constexpr auto A1ThreadSliceK0        = A1ThreadSlice_K0_M_K1[I0];
+        constexpr auto A1ThreadSliceM         = A1ThreadSlice_K0_M_K1[I1];
+        constexpr auto A1ThreadSliceK1        = A1ThreadSlice_K0_M_K1[I2];
+        constexpr auto a1_thread_desc_k0_m_k1 = make_naive_tensor_descriptor(
+            A1ThreadSlice_K0_M_K1,
+            make_tuple(A1ThreadSliceM * A1ThreadSliceK1, A1ThreadSliceK1, I1));
+
+        // B1 matrix in LDS memory, dst of blockwise copy
+        constexpr auto b1_block_desc_bk0_n_bk1 = GetB1BlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // A1 matrix blockwise copy
+        auto a1_blockwise_copy = ThreadwiseTensorSliceTransfer_StaticToStatic<
+            FloatGemmAcc,
+            FloatGemm,
+            decltype(acc_thread_desc_k0_m_k1),
+            decltype(a1_thread_desc_k0_m_k1),
+            tensor_operation::element_wise::PassThrough,
+            Sequence<A1ThreadSliceK0, A1ThreadSliceM, A1ThreadSliceK1>,
+            Sequence<1, 0, 2>,
+            2,
+            n4>{tensor_operation::element_wise::PassThrough{}};
+
+        // B1 matrix blockwise copy
+        auto b1_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<B1K0, Gemm1NPerBlock, B1K1>,
+                                                B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                B1BlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatGemm,
+                                                decltype(b1_grid_desc_bk0_n_bk1),
+                                                decltype(b1_block_desc_bk0_n_bk1),
+                                                B1BlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                B1BlockTransferSrcVectorDim,
+                                                2,
+                                                B1BlockTransferSrcScalarPerVector,
+                                                B1BlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                B1ThreadTransferSrcResetCoordinateAfterRun,
+                                                true, // DstResetCoord
+                                                NumGemmKPrefetchStage>(
+                b1_grid_desc_bk0_n_bk1,
+                make_multi_index(0, gemm1_n_block_data_idx_on_grid, 0),
+                b1_element_op,
+                b1_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        auto a1_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatGemm>(
+            a1_thread_desc_k0_m_k1.GetElementSpaceSize());
+
+        // reuse LDS space for gemm0's b_block_buf
+        auto b1_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatGemm*>(p_shared) + SharedMemTrait::b1_block_space_offset,
+            b1_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        // selected_mfma.group_size or B1K1 <= Gemm1KPack <= selected_mfma.group_size
+        // selected_mfma.k_per_blk <= Gemm1KPack
+        //
+        // Following similar rationale behind Gemm0KPack, let Gemm1KPack be the lowest common
+        // multiples of A1K1 (predetermined by selected_mfma.group_size) and B1K1. But in this case
+        // Gemm1KPack can't be higher than A1K1 itself because A1 matrix is distributed in VGPRs
+        // with 'group_size' amount of contiguous elements. Having Gemm1KPack greater than A1K1 will
+        // cause mismatch in summation index for example c[0:7] = a1[[0:3, 8:11]] * b1[0:7].
+        // therefore we may just as well assign Gemm1KPack = group_size
+        constexpr index_t Gemm1KPack =
+            MfmaSelector<FloatGemm, MPerXdl, NPerXdl>::selected_mfma.group_size;
+
+        auto gemm1_blockwise_gemm = BlockwiseGemmXdlops_v2<
+            BlockSize,
+            FloatGemm,
+            FloatGemmAcc,
+            decltype(a1_thread_desc_k0_m_k1),
+            decltype(b1_block_desc_bk0_n_bk1),
+            decltype(MakeGemm1AMmaTileDescriptor_M0_M1_M2_K(a1_thread_desc_k0_m_k1)),
+            decltype(MakeGemm1BMmaTileDescriptor_N0_N1_N2_K(b1_block_desc_bk0_n_bk1)),
+            MPerBlock,
+            Gemm1NPerBlock,
+            Gemm1KPerBlock,
+            MPerXdl,
+            NPerXdl,
+            MXdlPerWave,
+            Gemm1NXdlPerWave,
+            Gemm1KPack,
+            true,       // TransposeC
+            Gemm1KPack, // AMmaKStride
+            Gemm1KPack * XdlopsGemm<FloatGemm, MPerXdl, NPerXdl, Gemm1KPack, false>{}.K0PerXdlops>{
+            // BMmaKStride
+            make_tuple(0, 0, 0, 0)}; // A_origin
+
+        auto acc1_thread_buf = gemm1_blockwise_gemm.GetCThreadBuffer();
+
+        //
+        // Blockwise softmax
+        //
+        auto workspace_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatGemmAcc*>(p_shared) + SharedMemTrait::reduction_space_offset,
+            SharedMemTrait::reduction_space_size_aligned);
+
+        // get acc0 8D thread cluster
+        constexpr auto thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4 =
+            blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLengths() /
+            blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLengths();
+        constexpr auto tm0 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I0);
+        constexpr auto tn0 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I1);
+        constexpr auto tm1 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I2);
+        constexpr auto tn1 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I3);
+        constexpr auto tm2 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I4);
+        constexpr auto tn2 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I5);
+        constexpr auto tn3 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I6);
+        constexpr auto tn4 = thread_cluster_m0_n0_m1_n1_m2_n2_n3_n4.At(I7);
+
+        // get acc0 thread map
+        constexpr auto m0_n_m1_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(tm0 * tm1, tm2)),
+                       make_pass_through_transform(I1)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        constexpr auto threadid_to_m0_n_m1_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(
+                make_merge_transform(make_tuple(tm0 * tm1, tn0 * tn1 * tn2 * tn3 * tn4, tm2))),
+            make_tuple(Sequence<0, 1, 2>{}),
+            make_tuple(Sequence<0>{}));
+        const auto threadid_to_m_n_thread_cluster_adaptor =
+            chain_tensor_adaptors(m0_n_m1_to_m_n_adaptor, threadid_to_m0_n_m1_adaptor);
+
+        // get acc0 2D thread cluster & 2D thread slice
+        constexpr auto thread_cluster_desc_m_n = make_naive_tensor_descriptor_packed(
+            make_tuple(tm0 * tm1 * tm2, tn0 * tn1 * tn2 * tn3 * tn4));
+        constexpr auto thread_slice_desc_m_n =
+            make_naive_tensor_descriptor_packed(make_tuple(m0 * m1 * m2, n0 * n1 * n2 * n3 * n4));
+
+        auto blockwise_softmax = BlockwiseSoftmax<BlockSize,
+                                                  FloatGemmAcc,
+                                                  decltype(threadid_to_m_n_thread_cluster_adaptor),
+                                                  decltype(thread_cluster_desc_m_n),
+                                                  decltype(thread_slice_desc_m_n)>{};
+
+        auto blockwise_dropout = BlockwiseDropout<FloatGemmAcc, decltype(thread_slice_desc_m_n)>{
+            p_dropout_in_16bits, p_dropout_rescale};
+
+        const index_t num_gemm1_k_block_outer_loop =
+            b_grid_desc_bk0_n_bk1.GetLength(I1) / NPerBlock;
+        constexpr index_t num_gemm1_k_block_inner_loop = NPerBlock / Gemm1KPerBlock;
+
+        // Initialize C
+        StaticBuffer<AddressSpaceEnum::Vgpr, FloatGemmAcc, acc1_thread_buf.Size(), true>
+            c_thread_buf;
+        c_thread_buf.Clear();
+
+        // Initialize running sum and max of exponentiating row vectors
+        using SoftmaxBuf = typename decltype(blockwise_softmax)::BufferType;
+        SoftmaxBuf running_sum, running_sum_new, running_max, running_max_new;
+        running_sum     = 0;
+        running_sum_new = 0;
+        running_max     = NumericLimits<FloatGemmAcc>::Lowest();
+        running_max_new = NumericLimits<FloatGemmAcc>::Lowest();
+
+        auto lse_grid_desc_mblock_mrepeat_mwave_mperxdl =
+            MakeLSEGridDescriptor_MBlock_MRepeat_NWave_MPerXdl(lse_grid_desc_m);
+
+        constexpr auto lse_thread_desc_mblock_mrepeat_mwave_mperxdl =
+            make_naive_tensor_descriptor_packed(make_tuple(I1, m0, m1, m2));
+
+        auto lse_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatLSE>(
+            lse_thread_desc_mblock_mrepeat_mwave_mperxdl.GetElementSpaceSize());
+
+        auto acc0_thread_origin = blockwise_gemm.CalculateCThreadOriginDataIndex8D(
+            Number<0>{}, Number<0>{}, Number<0>{}, Number<0>{});
+        auto lse_thread_copy_vgpr_to_global = ThreadwiseTensorSliceTransfer_v1r3<
+            FloatGemmAcc,
+            FloatLSE,
+            decltype(lse_thread_desc_mblock_mrepeat_mwave_mperxdl),
+            decltype(lse_grid_desc_mblock_mrepeat_mwave_mperxdl),
+            ck::tensor_operation::element_wise::PassThrough,
+            Sequence<1, 1, 1, 1>,
+            Sequence<0, 1, 2, 3>,
+            3,
+            1,
+            InMemoryDataOperationEnum::Set,
+            1,
+            false>{lse_grid_desc_mblock_mrepeat_mwave_mperxdl,
+                   make_multi_index(block_work_idx_m,        // mblock
+                                    0,                       // mrepeat
+                                    acc0_thread_origin[I2],  // mwave
+                                    acc0_thread_origin[I4]), // mperxdl
+                   ck::tensor_operation::element_wise::PassThrough{}};
+
+        // gemm1 K loop
+        index_t gemm1_k_block_outer_index = 0;
+
+        // z is random number matrix for dropout verify
+        //
+        // z vgpr copy to global
+        //
+        // z matrix threadwise desc
+
+        // if(get_thread_global_1d_id()==0){
+        //    printf("m2 is %d \n",m2.value);
+        //    printf("n2 is %d \n",n2.value);
+        //    printf("n3 is %d \n",n3.value);
+        //    printf("n4 is %d \n",n4.value);
+        //}
+
+        constexpr auto z_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4 =   // for blockwise copy
+            make_naive_tensor_descriptor_packed(make_tuple(m0,   // MRepeat
+                                                           n0,   // NRepeat
+                                                           m1,   // MWaveId
+                                                           n1,   // NWaveId
+                                                           m2,   // MPerXdl
+                                                           n2,   // NGroupNum
+                                                           n3,   // NInputNum
+                                                           n4)); // registerNum
+
+        constexpr auto z_thread_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4 = // for blockwise copy
+            make_naive_tensor_descriptor_packed(make_tuple(m0,            //
+                                                           n0,            //
+                                                           m1,            //
+                                                           n1,            //
+                                                           m2,            // m0 1
+                                                           n2,            // n0 4
+                                                           n3,            // n1 1
+                                                           n4,            // m1 4
+                                                           I1));          // n2 1
+
+        constexpr auto z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5 =
+            make_naive_tensor_descriptor_packed(make_tuple(I1,   // MBlockId
+                                                           I1,   // NBlockID
+                                                           m0,   // MRepeat
+                                                           n0,   // NRepeat
+                                                           m1,   // MWaveId
+                                                           n1,   // NWaveId
+                                                           m2,   // MPerXdl
+                                                           n2,   // NGroupNum
+                                                           n3,   // NInputNum
+                                                           n4)); // registerNum
+
+        constexpr auto z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4 =
+            blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+
+        // constexpr auto z_block_lengths = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLengths();
+
+        constexpr auto zM0 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I0);
+        constexpr auto zN0 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I1);
+        constexpr auto zM1 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I2);
+        constexpr auto zN1 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I3);
+        constexpr auto zM2 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I4);
+        constexpr auto zN2 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I5);
+        constexpr auto zN3 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I6);
+        constexpr auto zN4 = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I7);
+
+        constexpr auto z_block_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4 =
+            transform_tensor_descriptor(
+                z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                make_tuple(make_pass_through_transform(zM0),
+                           make_pass_through_transform(zN0),
+                           make_pass_through_transform(zM1),
+                           make_pass_through_transform(zN1),
+                           make_unmerge_transform(make_tuple(Number<zM2.value / zN4.value>{}, zN4)),
+                           make_pass_through_transform(zN2),
+                           make_pass_through_transform(zN3),
+                           make_pass_through_transform(zN4)),
+                make_tuple(Sequence<0>{},
+                           Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<3>{},
+                           Sequence<4>{},
+                           Sequence<5>{},
+                           Sequence<6>{},
+                           Sequence<7>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<3>{},
+                           Sequence<4, 7>{},
+                           Sequence<5>{},
+                           Sequence<6>{},
+                           Sequence<8>{}));
+
+        // ignore = z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4;
+        // ignore = z_block_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4;
+
+        StaticBuffer<AddressSpaceEnum::Vgpr,
+                     unsigned short,
+                     z_thread_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4.GetElementSpaceSize(),
+                     true>
+            z_tensor_buffer; // z buffer after shuffle
+        z_tensor_buffer.Clear();
+
+        // z matrix global desc
+        auto z_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_z_grid, z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5.GetElementSpaceSize());
+
+        auto z_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<ZDataType*>(p_shared),
+            z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetElementSpaceSize());
+
+        // if(get_thread_global_1d_id()==0){
+        //     printf("z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetElementSpaceSize() is %ld \n",
+        //     z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetElementSpaceSize().value);
+        //     printf("z_block_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4.GetElementSpaceSize() is %ld
+        //     \n", z_block_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4.GetElementSpaceSize().value);
+        //     printf("z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5.GetElementSpaceSize() is %ld
+        //     \n",z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5.GetElementSpaceSize().value);
+        //     printf("z_thread_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4.GetElementSpaceSize() is %ld
+        //     \n",z_thread_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4.GetElementSpaceSize().value);
+        // }
+
+        const auto wave_id     = GetGemm0WaveIdx();
+        const auto wave_m_n_id = GetGemm0WaveMNIdx(wave_id[I2]); // I2: 0~63
+
+        // if(get_block_1d_id()==0){
+        //    if(get_thread_local_1d_id()==0){
+        //        printf("tid = 0 , wave_m_n_id[I0] & wave_m_n_id[I1] is %d & %d
+        //        \n",wave_m_n_id[I0], wave_m_n_id[I1]);
+        //    }
+        //    if(get_thread_local_1d_id()==1){
+        //        printf("tid = 1 , wave_m_n_id[I0] & wave_m_n_id[I1] is %d & %d
+        //        \n",wave_m_n_id[I0], wave_m_n_id[I1]);
+        //    }
+        //    if(get_thread_local_1d_id()==2){
+        //        printf("tid = 2 , wave_m_n_id[I0] & wave_m_n_id[I1] is %d & %d
+        //        \n",wave_m_n_id[I0], wave_m_n_id[I1]);
+        //    }
+        //    if(get_thread_local_1d_id()==3){
+        //        printf("tid = 3 , wave_m_n_id[I0] & wave_m_n_id[I1] is %d & %d
+        //        \n",wave_m_n_id[I0], wave_m_n_id[I1]);
+        //    }
+        //    if(get_thread_local_1d_id()==32){
+        //        printf("tid = 32 , wave_m_n_id[I0] & wave_m_n_id[I1] is %d & %d
+        //        \n",wave_m_n_id[I0], wave_m_n_id[I1]);
+        //    }
+        //    if(get_thread_local_1d_id()==64){
+        //        printf("tid = 32 , wave_m_n_id[I0] & wave_m_n_id[I1] is %d & %d
+        //        \n",wave_m_n_id[I0], wave_m_n_id[I1]);
+        //    }
+        //}
+
+        auto z_tmp_thread_copy_vgpr_to_lds =
+            ThreadwiseTensorSliceTransfer_v1r3<ushort,
+                                               ZDataType,
+                                               decltype(z_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                               decltype(z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                               tensor_operation::element_wise::PassThrough,
+                                               Sequence<m0, // MRepeat
+                                                        n0, // NRepeat
+                                                        m1, // MWaveId
+                                                        n1, // NWaveId
+                                                        m2, // MPerXdl
+                                                        n2, // NGroupNum
+                                                        n3, // NInputNum
+                                                        n4>,
+                                               Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                               7, // DstVectorDim
+                                               1, // DstScalarPerVector
+                                               InMemoryDataOperationEnum::Set,
+                                               1, // DstScalarStrideInVector
+                                               true>{
+                z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                make_multi_index(0,               // mrepeat
+                                 0,               // nrepeat
+                                 wave_id[I0],     // MWaveId
+                                 wave_id[I1],     // NWaveId
+                                 wave_m_n_id[I1], // MPerXdl
+                                 0,               // group
+                                 wave_m_n_id[I0], // NInputIndex
+                                 0),
+                tensor_operation::element_wise::PassThrough{}};
+
+        auto z_shuffle_thread_copy_lds_to_vgpr = ThreadwiseTensorSliceTransfer_v2<
+            ZDataType,
+            ushort,
+            decltype(z_block_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4),
+            decltype(z_thread_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4),
+            Sequence<m0, n0, m1, n1, m2, n2, n3, n4, I1>,
+            Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8>,
+            8,
+            1,
+            1,
+            true>{z_block_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4,
+                  make_multi_index(0,                        // mrepeat
+                                   0,                        // nrepeat
+                                   wave_id[I0],              // MWaveId
+                                   wave_id[I1],              // NWaveId
+                                   int(wave_m_n_id[I1] / 4), // MPerXdl
+                                   0,                        // group
+                                   wave_m_n_id[I0],          // NInputIndex
+                                   0,
+                                   wave_m_n_id[I1] % 4)};
+
+        auto z_thread_copy_vgpr_to_global = ThreadwiseTensorSliceTransfer_v1r3<
+            ushort,
+            ZDataType,
+            decltype(z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5),
+            decltype(z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5),
+            tensor_operation::element_wise::PassThrough,
+            Sequence<I1, // MBlockId
+                     I1, // NBlockID
+                     m0, // MRepeat
+                     n0, // NRepeat
+                     m1, // MWaveId
+                     n1, // NWaveId
+                     m2, // MPerXdl
+                     n2, // NGroupNum
+                     n3, // NInputNum
+                     n4>,
+            Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>,
+            9, // DstVectorDim
+            1, // DstScalarPerVector
+            InMemoryDataOperationEnum::Set,
+            1, // DstScalarStrideInVector
+            true>{z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                  make_multi_index(block_work_idx_m, // MBlockId
+                                   0,                // NBlockId
+                                   0,                // mrepeat
+                                   0,                // nrepeat
+                                   wave_id[I0],      // MWaveId
+                                   wave_id[I1],      // NWaveId
+                                   wave_m_n_id[I1],  // MPerXdl
+                                   0,                // group
+                                   wave_m_n_id[I0],  // NInputIndex
+                                   0),
+                  tensor_operation::element_wise::PassThrough{}};
+
+        if constexpr(Deterministic)
+        {
+            block_sync_lds();
+        }
+
+        do
+        {
+            auto n_block_data_idx_on_grid =
+                __builtin_amdgcn_readfirstlane(gemm1_k_block_outer_index * NPerBlock);
+            if(c0_matrix_mask.IsTileSkippable(
+                   m_block_data_idx_on_grid, n_block_data_idx_on_grid, MPerBlock, NPerBlock))
+            {
+                z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                    z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                    make_multi_index(0, 1, 0, 0, 0, 0, 0, 0, 0, 0));
+
+                continue;
+            }
+            // gemm0
+            gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
+                                                                   a_block_desc_ak0_m_ak1,
+                                                                   a_blockwise_copy,
+                                                                   a_grid_buf,
+                                                                   a_block_buf,
+                                                                   a_block_slice_copy_step,
+                                                                   b_grid_desc_bk0_n_bk1,
+                                                                   b_block_desc_bk0_n_bk1,
+                                                                   b_blockwise_copy,
+                                                                   b_grid_buf,
+                                                                   b_block_buf,
+                                                                   b_block_slice_copy_step,
+                                                                   blockwise_gemm,
+                                                                   acc_thread_buf,
+                                                                   num_k_block_main_loop);
+
+            // do MNK padding or upper triangular masking
+            if constexpr(MaskOutUpperTriangle || PadN)
+            {
+                // 8d thread_desc in thread scope
+                constexpr auto c_thread_lengths =
+                    blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLengths();
+
+                // 8d block_desc in block scope
+                constexpr auto c_block_lengths =
+                    blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLengths();
+
+                constexpr auto M0 = c_block_lengths[I0];
+                constexpr auto N0 = c_block_lengths[I1];
+                constexpr auto M1 = c_block_lengths[I2];
+                constexpr auto N1 = c_block_lengths[I3];
+                constexpr auto M2 = c_block_lengths[I4];
+                constexpr auto N2 = c_block_lengths[I5];
+                constexpr auto N3 = c_block_lengths[I6];
+                constexpr auto N4 = c_block_lengths[I7];
+
+                // works like multi-dimension static_for (static_ford), but provides both the linear
+                // index as well as n-d index
+                using Acc0TileIterator = SpaceFillingCurve<
+                    decltype(c_thread_lengths),
+                    typename arithmetic_sequence_gen<0, c_thread_lengths.Size(), 1>::type,
+                    typename uniform_sequence_gen<c_thread_lengths.Size(), 1>::type,
+                    false>; // SnakeCurved
+
+                constexpr auto block_idx_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+                    make_tuple(make_unmerge_transform(make_tuple(M0, M1, M2)),
+                               make_unmerge_transform(make_tuple(N0, N1, N2, N3, N4))),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                    make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5, 6, 7>{}));
+
+                static_for<0, Acc0TileIterator::GetNumOfAccess(), 1>{}([&](auto i) {
+                    auto acc0_thread_idx = Acc0TileIterator::GetIndex(i) + acc0_thread_origin;
+                    auto m_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                    auto n_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                    auto m_global = m_local + m_block_data_idx_on_grid;
+                    auto n_global = n_local + n_block_data_idx_on_grid;
+                    if(c0_matrix_mask.IsMaskedElement(m_global, n_global))
+                    {
+                        acc_thread_buf(i) = -ck::NumericLimits<float>::Infinity();
+                    }
+                    else
+                    {
+                        acc_element_op(acc_thread_buf(i), acc_thread_buf[i]);
+                    }
+                });
+            }
+            else
+            {
+                static_for<0, acc_thread_buf.Size(), 1>{}(
+                    [&](auto i) { acc_element_op(acc_thread_buf(i), acc_thread_buf[i]); });
+            }
+
+            block_sync_lds(); // wait for lds read in gemm0 blockwise gemm
+
+            // softmax
+            SoftmaxBuf& max = blockwise_softmax.max_value_buf;
+            SoftmaxBuf& sum = blockwise_softmax.sum_value_buf;
+
+            blockwise_softmax.Run(acc_thread_buf, workspace_buf);
+
+            if constexpr(IsDropout) // dropout
+            {
+                // 8d thread_desc in thread scope
+                constexpr auto c_thread_lengths =
+                    blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLengths();
+
+                // 8d block_desc in block scope
+                constexpr auto c_block_lengths =
+                    blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4().GetLengths();
+
+                constexpr auto M0 = c_block_lengths[I0];
+                constexpr auto N0 = c_block_lengths[I1];
+                constexpr auto M1 = c_block_lengths[I2];
+                constexpr auto N1 = c_block_lengths[I3];
+                constexpr auto M2 = c_block_lengths[I4];
+                constexpr auto N2 = c_block_lengths[I5];
+                constexpr auto N3 = c_block_lengths[I6];
+                constexpr auto N4 = c_block_lengths[I7];
+
+                // works like multi-dimension static_for (static_ford), but provides both the linear
+                // index as well as n-d index
+                using Acc0TileIterator = SpaceFillingCurve<
+                    decltype(c_thread_lengths),
+                    typename arithmetic_sequence_gen<0, c_thread_lengths.Size(), 1>::type,
+                    typename uniform_sequence_gen<c_thread_lengths.Size(), 1>::type,
+                    false>; // SnakeCurved
+
+                constexpr auto block_idx_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+                    make_tuple(make_unmerge_transform(make_tuple(M0, M1, M2)),
+                               make_unmerge_transform(make_tuple(N0, N1, N2, N3, N4))),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                    make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5, 6, 7>{}));
+
+                auto acc0_thread_idx = Acc0TileIterator::GetIndex(I0) + acc0_thread_origin;
+                auto m_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                auto n_local  = block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                auto m_global = m_local + m_block_data_idx_on_grid;
+                auto n_global = n_local + n_block_data_idx_on_grid;
+
+                auto global_elem_id =
+                    MRaw * NRaw * g_idx + m_global * NRaw + n_global; // unique element global 1d id
+
+                // index_t id_step = Acc0TileIterator::GetNumOfAccess() / n0.value;
+
+                // save z to global
+
+                blockwise_dropout.template GenerateZMatrixAttnFwd<decltype(z_tensor_buffer)>(
+                    ph, global_elem_id, z_tensor_buffer);
+
+                z_tmp_thread_copy_vgpr_to_lds.Run(z_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                                  make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
+                                                  z_tensor_buffer,
+                                                  z_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                                  z_block_buf);
+
+                z_shuffle_thread_copy_lds_to_vgpr.Run(
+                    z_block_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4,
+                    z_block_buf,
+                    z_thread_shuffle_desc_m0_n0_m1_n1_m2_n2_n3_m3_n4,
+                    make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0),
+                    z_tensor_buffer);
+
+                blockwise_dropout.template ApplyDropoutWithZ<decltype(acc_thread_buf),
+                                                             decltype(z_tensor_buffer),
+                                                             false>(acc_thread_buf,
+                                                                    z_tensor_buffer);
+
+                if(p_z_grid)
+                {
+                    // ignore = z_tensor_buffer;
+                    z_thread_copy_vgpr_to_global.Run(
+                        z_thread_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                        make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
+                        z_tensor_buffer,
+                        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                        z_grid_buf);
+
+                    z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_n3_n4_n5,
+                        make_multi_index(0, 1, 0, 0, 0, 0, 0, 0, 0, 0));
+                }
+            }
+
+            // TODO: may convert to log domain
+            running_max_new = mathext::max(max, running_max);
+            running_sum_new = mathext::exp(running_max - running_max_new) * running_sum +
+                              mathext::exp(max - running_max_new) * sum;
+
+            // gemm1
+            {
+                // TODO: explore using dynamic buffer for a1 thread buffer
+                // For a1_blockwise_copy, the goal is to satisfy pipeline requirements RunRead(),
+                // RunWrite(), and MoveSliceWindow(). But it is impossible to implement given that
+                // the A1 source buffer is static buffer holding the output of first GEMM and
+                // requires constexpr offset by design. Therefore, we pass tensor coordinate offset
+                // explicitly in Run() below.
+
+                // Initialize acc1
+                acc1_thread_buf.Clear();
+
+                // preload data into LDS
+                b1_blockwise_copy.RunRead(b1_grid_desc_bk0_n_bk1, b1_grid_buf);
+
+                b1_blockwise_copy.MoveSrcSliceWindow(b1_grid_desc_bk0_n_bk1,
+                                                     b1_block_slice_copy_step);
+
+                block_sync_lds(); // wait for reduction LDS read
+
+                b1_blockwise_copy.RunWrite(b1_block_desc_bk0_n_bk1, b1_block_buf);
+
+                // main body
+                if constexpr(num_gemm1_k_block_inner_loop > 1)
+                {
+                    static_for<0, num_gemm1_k_block_inner_loop - 1, 1>{}([&](auto i) {
+                        a1_blockwise_copy.Run(acc_thread_desc_k0_m_k1,
+                                              make_tuple(Number<i * A1ThreadSliceK0>{}, I0, I0),
+                                              acc_thread_buf,
+                                              a1_thread_desc_k0_m_k1,
+                                              make_tuple(I0, I0, I0),
+                                              a1_thread_buf);
+
+                        b1_blockwise_copy.RunRead(b1_grid_desc_bk0_n_bk1, b1_grid_buf);
+
+                        block_sync_lds();
+
+                        gemm1_blockwise_gemm.Run(a1_thread_buf, b1_block_buf, acc1_thread_buf);
+
+                        block_sync_lds();
+
+                        b1_blockwise_copy.MoveSrcSliceWindow(b1_grid_desc_bk0_n_bk1,
+                                                             b1_block_slice_copy_step);
+
+                        b1_blockwise_copy.RunWrite(b1_block_desc_bk0_n_bk1, b1_block_buf);
+                    });
+                }
+                // tail
+                {
+                    a1_blockwise_copy.Run(
+                        acc_thread_desc_k0_m_k1,
+                        make_tuple(
+                            Number<(num_gemm1_k_block_inner_loop - 1) * A1ThreadSliceK0>{}, I0, I0),
+                        acc_thread_buf,
+                        a1_thread_desc_k0_m_k1,
+                        make_tuple(I0, I0, I0),
+                        a1_thread_buf);
+
+                    block_sync_lds();
+
+                    gemm1_blockwise_gemm.Run(a1_thread_buf, b1_block_buf, acc1_thread_buf);
+                }
+            } // end gemm1
+
+            // workaround compiler issue; see ck/ck.hpp
+            if constexpr(CK_WORKAROUND_SWDEV_XXXXXX_BF16_ATTEN_FWD_GFX908_ISSUE == 1 &&
+                         (is_same_v<FloatGemm, bhalf_t>)&&MPerBlock == 256 && NPerBlock == 128 &&
+                         Gemm1NPerBlock == 128)
+            {
+                __builtin_amdgcn_sched_barrier(0);
+            }
+
+            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4 =
+                gemm1_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+            constexpr auto cm0 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I0);
+            constexpr auto cn0 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I1);
+            constexpr auto cm1 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I2);
+            constexpr auto cn1 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I3);
+            constexpr auto cm2 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I4);
+            constexpr auto cn2 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I5);
+            constexpr auto cn3 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I6);
+            constexpr auto cn4 = c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLength(I7);
+            constexpr auto c_thread_slice_desc_m_n = make_naive_tensor_descriptor_packed(
+                make_tuple(cm0 * cm1 * cm2, cn0 * cn1 * cn2 * cn3 * cn4));
+            constexpr auto c_thread_buf_slice_m = c_thread_slice_desc_m_n.GetLength(I0);
+            constexpr auto c_thread_buf_slice_n = c_thread_slice_desc_m_n.GetLength(I1);
+
+            static_for<0, c_thread_buf_slice_m, 1>{}([&](auto iM) {
+                static_for<0, c_thread_buf_slice_n, 1>{}([&](auto iN) {
+                    auto I = Number<c_thread_slice_desc_m_n.CalculateOffset(make_tuple(iM, iN))>{};
+                    FloatGemmAcc acc1 = acc1_thread_buf[I]; // P*V
+                    FloatGemmAcc c    = c_thread_buf[I];    // O
+                    FloatGemmAcc c_new =
+                        (running_sum[iM] * math::exp(running_max[iM] - running_max_new[iM]) * c +
+                         math::exp(max[iM] - running_max_new[iM]) * acc1) /
+                        running_sum_new[iM]; // Formula by Dao et al.,
+                                             // https://arxiv.org/pdf/2205.14135v2.pdf section 3.1
+
+                    c_thread_buf(I) = c_new; // O_new
+                });
+            });
+
+            a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_ak0_m_ak1,
+                                                a_block_reset_copy_step); // rewind K
+            b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_bk0_n_bk1,
+                                                b_block_reset_copy_step); // rewind K and step N
+
+            // update before next j iteration
+            running_max = running_max_new;
+            running_sum = running_sum_new;
+
+            block_sync_lds(); // wait for gemm1 LDS read
+        } while(++gemm1_k_block_outer_index < num_gemm1_k_block_outer_loop); // end j loop
+
+        // Calculate max + ln(sum) and write out
+
+        if constexpr(IsLseStoring)
+        {
+            static_for<0, MXdlPerWave, 1>{}(
+                [&](auto I) { lse_thread_buf(I) = running_max(I) + math::log(running_sum(I)); });
+
+            if(get_warp_local_1d_id() < AccM2)
+            {
+                static_for<0, MXdlPerWave, 1>{}([&](auto I) {
+                    // copy from VGPR to Global
+                    lse_thread_copy_vgpr_to_global.Run(lse_thread_desc_mblock_mrepeat_mwave_mperxdl,
+                                                       make_tuple(I0, Number<I>{}, I0, I0),
+                                                       lse_thread_buf,
+                                                       lse_grid_desc_mblock_mrepeat_mwave_mperxdl,
+                                                       lse_grid_buf);
+
+                    lse_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                        lse_grid_desc_mblock_mrepeat_mwave_mperxdl, make_multi_index(0, 1, 0, 0));
+                });
+            }
+        }
+
+        // shuffle C and write out
+        {
+            static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
+                              Gemm1NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
+                          "wrong!");
+
+            constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+            constexpr index_t NWave = Gemm1NPerBlock / (Gemm1NXdlPerWave * NPerXdl);
+
+            // TODO: hacky, fix it!
+            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4 =
+                gemm1_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+
+            // TODO: hacky, fix it!
+            // c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp is only used to get lengths
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp =
+                gemm1_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+
+            constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I0);
+            constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I1);
+            constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I2);
+            constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I3);
+            constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I4);
+            constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I5);
+            constexpr auto N3 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I6);
+            constexpr auto N4 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I7);
+
+            constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+                GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<FloatCShuffle*>(p_shared),
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4 = transform_tensor_descriptor(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
+                        M1,                                      // M1 = MWave
+                        M2)),                                    // M2 = MPerXdl
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
+                        N1,                                      // N1 = NWave
+                        N2,                                      // N2 * N3 * N4 = NPerXdl
+                        N3,
+                        N4))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(
+                    Sequence<>{}, Sequence<0, 2, 4>{}, Sequence<>{}, Sequence<1, 3, 5, 6, 7>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            const auto c_thread_mtx_on_block =
+                gemm1_blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_m0_m1_m2_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2))),
+                    make_tuple(Sequence<0, 1, 2>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto m_thread_data_on_block_idx =
+                m_thread_data_on_block_to_m0_m1_m2_adaptor.CalculateBottomIndex(
+                    make_multi_index(m_thread_data_on_block));
+
+            const auto n_thread_data_on_block_to_n0_n1_n2_n3_n4_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2, N3, N4))),
+                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_idx =
+                n_thread_data_on_block_to_n0_n1_n2_n3_n4_adaptor.CalculateBottomIndex(
+                    make_multi_index(n_thread_data_on_block));
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto c_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
+                                                   FloatCShuffle,
+                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                                   tensor_operation::element_wise::PassThrough,
+                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
+                                                            CShuffleNXdlPerWavePerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            I1,
+                                                            N2,
+                                                            I1,
+                                                            N4>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     n_thread_data_on_block_idx[I2],
+                                     n_thread_data_on_block_idx[I3],
+                                     n_thread_data_on_block_idx[I4]),
+                    tensor_operation::element_wise::PassThrough{}};
+
+            // shuffle: blockwise copy C from LDS to global
+            auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // ThreadGroup
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                         1,
+                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
+                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                FloatCShuffle,        // typename SrcData,
+                FloatC,               // typename DstData,
+                decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
+                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<0, 1, 2, 3>,                           // typename DimAccessOrder,
+                3,                                              // index_t VectorDim,
+                CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun>
+                {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(0, 0, 0, 0),
+                 c_grid_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(block_work_idx_m, 0, block_work_idx[I1], 0),
+                 c_element_op};
+
+            // space filling curve for threadwise C in VGPR
+            constexpr auto sfc_c_vgpr =
+                SpaceFillingCurve<Sequence<MXdlPerWave, Gemm1NXdlPerWave, 1, 1, 1, N2, 1, N4>,
+                                  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                  Sequence<CShuffleMXdlPerWavePerShuffle,
+                                           CShuffleNXdlPerWavePerShuffle,
+                                           1,
+                                           1,
+                                           1,
+                                           N2,
+                                           1,
+                                           N4>>{};
+
+            // space filling curve for shuffled blockwise C in global mem
+            constexpr auto sfc_c_global =
+                SpaceFillingCurve<Sequence<1, MPerBlock, 1, Gemm1NPerBlock>,
+                                  Sequence<0, 2, 1, 3>,
+                                  Sequence<1,
+                                           CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                                           1,
+                                           CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
+
+            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
+
+            static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
+
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                              sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                              c_thread_buf,
+                                              c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                              c_shuffle_block_buf);
+
+                // make sure it's safe to read from LDS
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                c_shuffle_block_copy_lds_to_global.Run(
+                    c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                    c_shuffle_block_buf,
+                    c_grid_desc_mblock_mperblock_nblock_nperblock,
+                    c_grid_buf);
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
+
+                    // move on C
+                    c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
+                        c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
+                }
+            });
+        }
+    }
+};
+
+} // namespace ck

include/ck/utility/philox_rand.hpp

@@ -84,6 +84,17 @@ class philox
         out_tmp[3] = tmp_ph.w;
     }
 
+    __device__ void get_random_4x16(ushort* out, const unsigned long long subsequence)
+    {
+        uint4 tmp_ph;
+        tmp_ph = get_philox_4x32(subsequence);
+
+        out[0] = static_cast<ushort>(tmp_ph.x);
+        out[1] = static_cast<ushort>(tmp_ph.y);
+        out[2] = static_cast<ushort>(tmp_ph.z);
+        out[3] = static_cast<ushort>(tmp_ph.w);
+    }
+
     private:
     struct ull2
     {