// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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 USING_MASK 0
#define DIM 64 // 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_grouped_mha_bwd_xdl_cshuffle_qloop_v1.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_mha_bwd_xdl_cshuffle_qloop_v2.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_mha_fwd_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 = void;
using Acc1BiasDataType = void;

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::MaskUpperTriangleFromTopLeft;
#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.
// If 32 < DIM <= 64 , ues prototype1.
// If 64 < DIM <= 128, ues prototype2.
#if(DIM <= 32)
// clang-format off
using DeviceGemmInstanceFWD =
    // #################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO|     ADataType|     BDataType|     B1DataType|     CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|  B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|   Step|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                           _NPerBlock|       SrcScalar|            |
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|       | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                     |       PerVector|            |
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|       |                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |                 |                |                |                |                |                |           |            |            |                             |                                     |                |            |
    ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType,  InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType,             void,             void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,    128,   128,    32,    32,    32,   8,   8,    2,   32,   32,     1,     4,     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,     S<16, 16, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               2,               2,      false,           1,           1,               S<1, 64, 1, 4>,                                    8,               1, MaskingSpec>;

using DeviceGemmInstanceBWD =
    // ########################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|  NXdl|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|   Per| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|  Wave|                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |            |            |                             |                                               |            |              |
    ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,   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,               4,           1,           1,               S<1, 64, 1, 4>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;
// clang-format on
#elif(DIM <= 64)
// clang-format off
using DeviceGemmInstanceFWD =
    // #################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO|     ADataType|     BDataType|     B1DataType|     CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|  B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec| 
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|   Step|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                           _NPerBlock|       SrcScalar|            | 
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|       | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                     |       PerVector|            |
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|       |                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |                 |                |                |                |                |                |           |            |            |                             |                                     |                |            |
    ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType,  InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType,             void,             void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,    128,   128,    32,    64,    32,   8,   8,    2,   32,   32,     1,     4,     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,     S<16, 16, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           2,               S<1, 32, 1, 8>,                                    8,               1, MaskingSpec>;

using DeviceGemmInstanceBWD =
    // ########################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|  NXdl|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|   Per| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|  Wave|                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |            |            |                             |                                               |            |              |
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,   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,               4,           1,           2,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;
    ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,    64,    32,    32,   8,   8,    2,   32,   32,     4,     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,               4,           1,           2,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;
    // ########################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|  B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|  NXdl|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|   Per| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|  Wave|                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |                 |                |                |                |                |                |           |            |            |                             |                                               |            |              |
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    64,    64,    32,    64,   8,   8,    2,   32,   32,     4,     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,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               2,               2,      false,           1,           2,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    32,    64,    32,    64,   8,   8,    2,   32,   32,     4,     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,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               2,               2,      false,           1,           2,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    64,    64,    32,    64,   8,   8,    2,   32,   32,     4,     1,     2,     2,     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,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               2,               2,      false,           1,           2,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;
// clang-format on
#elif(DIM <= 128)
// clang-format off
using DeviceGemmInstanceFWD =
    // #################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO|     ADataType|     BDataType|     B1DataType|     CDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1|Dropout|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|  B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector| D1BlockTransfer| MaskingSpec|
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|   Step|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                           _NPerBlock|       SrcScalar|            |
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|       | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                     |       PerVector|            |
    // #################################################################################|         |        |        |        |        |              |              |               |              |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|       |                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |                 |                |                |                |                |                |           |            |            |                             |                                     |                |            |
    ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle_V2<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, InputDataType,  InputDataType, InputDataType, GemmDataType, ZDataType, LSEDataType,             void,             void, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,    128,   128,    32,    64,    32,   8,   8,    2,   32,   32,     1,     4,     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,     S<16, 16, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           2,               S<1, 32, 1, 8>,                                    8,               1, MaskingSpec>;

using DeviceGemmInstanceBWD =
    // ########################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|  B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|  NXdl|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|   Per| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|  Wave|                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |                 |                |                |                |                |                |           |            |            |                             |                                               |            |              |
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    64,   128,    32,    64,   8,   8,    2,   32,   32,     4,     1,     4,     2,     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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    64,   128,    32,    64,   8,   8,    2,   32,   32,     4,     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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    32,   128,    32,    64,   8,   8,    2,   32,   32,     4,     1,     4,     2,     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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    32,   128,    32,    64,   8,   8,    2,   32,   32,     4,     1,     4,     4,     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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,   128,    32,   128,    32,    64,   8,   8,    2,   32,   32,     4,     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,               4,      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>;               

    ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,   128,    32,    64,   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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,   128,    32,    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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,   128,    32,   128,   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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,   128,    32,    64,   8,   8,    2,   32,   32,     2,     1,     4,     2,     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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    32,   128,    32,    64,   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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,    64,   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,               4,      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>;               

    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,     32,   128,   128,   128,    32,    64,   8,   8,    2,   32,   32,     1,     1,     4,     1,     S<8, 32, 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,               4,      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>;               
    // ck::tensor_operation::device::DeviceGroupedMultiheadAttentionBackward_Qloop_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,     32,   128,    64,   128,    32,    64,   8,   8,    2,   32,   32,     1,     1,     4,     1,     S<8, 32, 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,               4,      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>;
// clang-format on
#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_uint8_t,
                            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 M          = s_g_m_n.GetLengths()[1];
    auto N          = s_g_m_n.GetLengths()[2];
    const auto mask = DeviceGemmInstanceFWD::C0MatrixMask(M, 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_uint8_t, 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_g1q_m_o = reshape(y_g_m_o, [G0, G1Q, M, O])
    // y_g0_m_g1q_o = permute(y_g0_g1q_m_o, [0, 2, 1, 3])
    float alpha  = 1.f / std::sqrt(DIM);
    float p_drop = 0.2;
    int h_ratio  = 1; // G1Q / G1KV

    bool input_permute  = true;
    bool output_permute = true;

    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 == 8)
    {
        do_verification = std::stoi(argv[1]);
        init_method     = std::stoi(argv[2]);
        time_kernel     = std::stoi(argv[3]);

        p_drop  = std::stof(argv[4]);
        h_ratio = std::stof(argv[5]);

        input_permute  = std::stoi(argv[6]);
        output_permute = std::stoi(argv[7]);
    }
    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: p_drop\n");
        printf("arg5: h_ratio\n");
        printf("arg6 to 7: input / output permute\n");
        exit(0);
    }

    float p_dropout                = 1 - p_drop;
    ZDataType p_dropout_in_uint8_t = ZDataType(std::floor(p_dropout * 255.0));
    float rp_dropout               = 1.0 / p_dropout;

    auto gemm_fwd    = DeviceGemmInstanceFWD{};
    auto invoker_fwd = gemm_fwd.MakeInvoker();
    std::vector<DeviceGemmInstanceFWD::ProblemDesc> problem_descs_fwd;
    auto gemm_bwd    = DeviceGemmInstanceBWD{};
    auto invoker_bwd = gemm_bwd.MakeInvoker();
    std::vector<DeviceGemmInstanceBWD::ProblemDesc> problem_descs_bwd;

    using DeviceMemPtr = std::unique_ptr<DeviceMem>;
    std::vector<const void*> p_q;
    std::vector<const void*> p_k;
    std::vector<void*> p_z_fwd;     // for result verification
    std::vector<void*> p_z_bwd;     // for result verification
    std::vector<void*> p_z_nullptr; // for time test
    std::vector<const void*> p_v;
    std::vector<void*> p_y_fwd;
    std::vector<void*> p_lse_fwd;
    std::vector<const void*> p_y_bwd;
    std::vector<const void*> p_lse_bwd;
    std::vector<void*> p_qgrad;
    std::vector<void*> p_kgrad;
    std::vector<void*> p_vgrad;
    std::vector<const void*> p_ygrad;

    std::vector<Tensor<InputDataType>> q_g_m_ks;
    std::vector<Tensor<InputDataType>> k_g_n_ks;
    std::vector<Tensor<ZDataType>> z_fwd_g_m_ns;
    std::vector<Tensor<ZDataType>> z_bwd_g_m_ns;
    std::vector<Tensor<InputDataType>> v_g_n_os;
    std::vector<Tensor<AccDataType>> s_g_m_ns;
    std::vector<Tensor<InputDataType>> p_g_m_ns;
    std::vector<Tensor<InputDataType>> y_g_m_os;
    std::vector<Tensor<LSEDataType>> lse_g_ms;
    std::vector<Tensor<InputDataType>> p_drop_g_m_ns;

    std::vector<Tensor<InputDataType>> q_tensors;
    std::vector<Tensor<InputDataType>> k_tensors;
    std::vector<Tensor<InputDataType>> v_tensors;
    std::vector<Tensor<InputDataType>> y_tensors;
    std::vector<Tensor<ZDataType>> z_fwd_tensors;
    std::vector<Tensor<ZDataType>> z_bwd_tensors;
    std::vector<Tensor<LSEDataType>> lse_tensors;
    std::vector<Tensor<OutputDataType>> qgrad_tensors;
    std::vector<Tensor<OutputDataType>> kgrad_tensors;
    std::vector<Tensor<OutputDataType>> vgrad_tensors;
    std::vector<Tensor<InputDataType>> ygrad_tensors;

    std::vector<DeviceMemPtr> q_tensors_device;
    std::vector<DeviceMemPtr> k_tensors_device;
    std::vector<DeviceMemPtr> z_fwd_tensors_device;
    std::vector<DeviceMemPtr> z_bwd_tensors_device;
    std::vector<DeviceMemPtr> v_tensors_device;
    std::vector<DeviceMemPtr> y_tensors_device;
    std::vector<DeviceMemPtr> lse_tensors_device;
    std::vector<DeviceMemPtr> qgrad_tensors_device;
    std::vector<DeviceMemPtr> ygrad_tensors_device;
    std::vector<DeviceMemPtr> kgrad_tensors_device;
    std::vector<DeviceMemPtr> vgrad_tensors_device;
    std::size_t group_count = 12;
    std::size_t flop_fwd = 0, num_byte_fwd = 0;
    std::size_t flop_bwd = 0, num_byte_bwd = 0;
    for(std::size_t i = 0; i < group_count; i++)
    {
        int M    = 128 * (rand() % 8) + (rand() % 128);
        int N    = 128 * (rand() % 8) + (rand() % 128);
        int K    = DIM;
        int O    = DIM;
        int G0   = rand() % 4 + 1;
        int G1KV = rand() % 4 + 1;
        int G1Q  = G1KV * h_ratio;
        std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1Q, M, K};
        std::vector<ck::index_t> q_gs_ms_ks_strides =
            input_permute
                ? std::vector<ck::index_t>{M * G1Q * K, K, G1Q * K, 1} // Q layout [G0, M, G1Q, K]
                : std::vector<ck::index_t>{G1Q * M * K, M * K, K, 1};  // Q layout [G0, G1Q, M, K]

        std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1KV, N, K};
        std::vector<ck::index_t> k_gs_ns_ks_strides =
            input_permute
                ? std::vector<ck::index_t>{N * G1KV * K, K, G1KV * K, 1}
                // K layout [G0, N, G1KV, K]
                : std::vector<ck::index_t>{G1KV * N * K, N * K, K, 1}; // K layout [G0, G1KV, N, K]

        std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1KV, O, N};
        std::vector<ck::index_t> v_gs_os_ns_strides =
            input_permute
                ? std::vector<ck::index_t>{N * G1KV * O, O, 1, G1KV * O}
                // V layout [G0, N, G1KV, O]
                : std::vector<ck::index_t>{G1KV * N * O, N * O, 1, O}; // V layout [G0, G1KV, N, O]

        std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1Q, M, O};
        std::vector<ck::index_t> y_gs_ms_os_strides =
            output_permute
                ? std::vector<ck::index_t>{M * G1Q * O, O, G1Q * O, 1} // Y layout [G0, M, G1Q, O]
                : std::vector<ck::index_t>{G1Q * M * O, M * O, O, 1};  // Y layout [G0, G1Q, M, O]

        std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1Q, M, N};
        std::vector<ck::index_t> z_gs_ms_ns_strides =
            input_permute
                ? std::vector<ck::index_t>{M * G1Q * N, N, G1Q * N, 1} // Z layout [G0, M, G1Q, N]
                : std::vector<ck::index_t>{G1Q * M * N, M * N, N, 1};  // Z layout [G0, G1Q, M, N]
        std::vector<ck::index_t> kgrad_gs_ns_ks_lengths{G0, G1Q, N, K};
        std::vector<ck::index_t> kgrad_gs_ns_ks_strides =
            input_permute ? std::vector<ck::index_t>{N * G1Q * K, K, G1Q * K, 1}
                          // KGrad layout [G0, N, G1Q, K]
                          : std::vector<ck::index_t>{
                                G1Q * N * K, N * K, K, 1}; // KGrad layout [G0, G1Q, N, K]

        std::vector<ck::index_t> vgrad_gs_os_ns_lengths{G0, G1Q, O, N};
        std::vector<ck::index_t> vgrad_gs_os_ns_strides =
            input_permute ? std::vector<ck::index_t>{N * G1Q * O, O, 1, G1Q * O}
                          // VGrad layout [G0, N, G1Q, O]
                          : std::vector<ck::index_t>{
                                G1Q * N * O, N * O, 1, O}; // VGrad layout [G0, G1Q, N, O]
        // 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, G1Q, M};
        std::vector<ck::index_t> lse_gs_ms_strides{G1Q * M, M, 1}; // LSE layout [G0, G1Q, M]
        problem_descs_fwd.push_back({
            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,
            lse_gs_ms_strides,
            {}, // acc0_biases_gs_ms_ns_lengths
            {}, // acc0_biases_gs_ms_ns_strides
            {}, // acc1_biases_gs_ms_os_lengths
            {}, // acc1_biases_gs_ms_os_strides
        });
        problem_descs_bwd.push_back({
            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,
            lse_gs_ms_strides,
            kgrad_gs_ns_ks_lengths,
            kgrad_gs_ns_ks_strides,
            vgrad_gs_os_ns_lengths,
            vgrad_gs_os_ns_strides,
            {}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
            {}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
            {}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
            {}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_strides},
        });

        int BatchCount = G0 * G1Q;
        flop_fwd += (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * BatchCount;
        num_byte_fwd += (sizeof(InputDataType) * M * K + sizeof(InputDataType) * K * N +
                         sizeof(InputDataType) * N * O + sizeof(InputDataType) * M * O) *
                        BatchCount;

        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
        num_byte_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;

        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> y_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
        Tensor<InputDataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
        Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
        Tensor<OutputDataType> kgrad_gs_ns_ks(kgrad_gs_ns_ks_lengths, kgrad_gs_ns_ks_strides);
        Tensor<OutputDataType> vgrad_gs_os_ns(vgrad_gs_os_ns_lengths, vgrad_gs_os_ns_strides);
        if(i < 4)
        {
            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.mDesc << std::endl;
            std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
            std::cout << "kgrad_gs_ns_ks: " << kgrad_gs_ns_ks.mDesc << std::endl;
            std::cout << "vgrad_gs_os_ns: " << vgrad_gs_os_ns.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_Diagonal<InputDataType>{});
            v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
            ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{2});
            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, g1q, 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, g1q, 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, g1q, 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
        }

        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> y_g_m_o({BatchCount, M, O});
        Tensor<LSEDataType> lse_g_m({BatchCount, M});
        Tensor<InputDataType> p_drop_g_m_n({BatchCount, M, N});

        q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
            q_g_m_k(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
        });
        k_g_n_k.ForEach([&](auto& self, auto idx) {
            const size_t& g0   = idx[0] / G1Q;
            const size_t& g1q  = idx[0] % G1Q;
            const size_t& g1kv = g1q / h_ratio;

            self(idx) = k_gs_ns_ks(g0, g1kv, idx[1], idx[2]);
        });
        v_g_n_o.ForEach([&](auto& self, auto idx) {
            const size_t& g0   = idx[0] / G1Q;
            const size_t& g1q  = idx[0] % G1Q;
            const size_t& g1kv = g1q / h_ratio;

            self(idx) = v_gs_os_ns(g0, g1kv, idx[2], idx[1]);
        });

        q_g_m_ks.push_back(q_g_m_k);
        k_g_n_ks.push_back(k_g_n_k);
        z_fwd_g_m_ns.push_back(z_fwd_g_m_n);
        z_bwd_g_m_ns.push_back(z_bwd_g_m_n);
        v_g_n_os.push_back(v_g_n_o);
        s_g_m_ns.push_back(s_g_m_n);
        p_g_m_ns.push_back(p_g_m_n);
        y_g_m_os.push_back(y_g_m_o);
        lse_g_ms.push_back(lse_g_m);
        p_drop_g_m_ns.push_back(p_drop_g_m_n);

        q_tensors.push_back(q_gs_ms_ks);
        k_tensors.push_back(k_gs_ns_ks);
        v_tensors.push_back(v_gs_os_ns);
        y_tensors.push_back(y_gs_ms_os);
        z_fwd_tensors.push_back(z_fwd_gs_ms_ns);
        z_bwd_tensors.push_back(z_bwd_gs_ms_ns);
        lse_tensors.push_back(lse_gs_ms);
        ygrad_tensors.push_back(ygrad_gs_ms_os);
        kgrad_tensors.push_back(kgrad_gs_ns_ks);
        vgrad_tensors.push_back(vgrad_gs_os_ns);

        q_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(InputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
        k_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(InputDataType) * k_gs_ns_ks.GetElementSpaceSize()));
        z_fwd_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(ZDataType) * z_fwd_gs_ms_ns.GetElementSpaceSize()));
        z_bwd_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(ZDataType) * z_bwd_gs_ms_ns.GetElementSpaceSize()));
        v_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(InputDataType) * v_gs_os_ns.GetElementSpaceSize()));
        y_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(InputDataType) * y_gs_ms_os.GetElementSpaceSize()));
        lse_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(LSEDataType) * lse_gs_ms.GetElementSpaceSize()));
        qgrad_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(OutputDataType) * q_gs_ms_ks.GetElementSpaceSize()));
        kgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
            sizeof(OutputDataType) * kgrad_gs_ns_ks.GetElementSpaceSize()));
        vgrad_tensors_device.emplace_back(std::make_unique<DeviceMem>(
            sizeof(OutputDataType) * vgrad_gs_os_ns.GetElementSpaceSize()));
        ygrad_tensors_device.emplace_back(
            std::make_unique<DeviceMem>(sizeof(InputDataType) * y_gs_ms_os.GetElementSpaceSize()));

        q_tensors_device.back()->ToDevice(q_gs_ms_ks.data());
        k_tensors_device.back()->ToDevice(k_gs_ns_ks.data());
        z_fwd_tensors_device.back()->ToDevice(z_fwd_gs_ms_ns.data());
        z_bwd_tensors_device.back()->ToDevice(z_bwd_gs_ms_ns.data());
        v_tensors_device.back()->ToDevice(v_gs_os_ns.data());
        ygrad_tensors_device.back()->ToDevice(ygrad_gs_ms_os.data());

        p_q.push_back(q_tensors_device.back()->GetDeviceBuffer());
        p_k.push_back(k_tensors_device.back()->GetDeviceBuffer());
        p_z_fwd.push_back(z_fwd_tensors_device.back()->GetDeviceBuffer());
        p_z_bwd.push_back(z_bwd_tensors_device.back()->GetDeviceBuffer());
        p_z_nullptr.push_back(nullptr);
        p_v.push_back(v_tensors_device.back()->GetDeviceBuffer());
        p_y_fwd.push_back(y_tensors_device.back()->GetDeviceBuffer());
        p_lse_fwd.push_back(lse_tensors_device.back()->GetDeviceBuffer());
        p_y_bwd.push_back(y_tensors_device.back()->GetDeviceBuffer());
        p_lse_bwd.push_back(lse_tensors_device.back()->GetDeviceBuffer());
        p_kgrad.push_back(kgrad_tensors_device.back()->GetDeviceBuffer());
        p_vgrad.push_back(vgrad_tensors_device.back()->GetDeviceBuffer());
        p_ygrad.push_back(ygrad_tensors_device.back()->GetDeviceBuffer());
        p_qgrad.push_back(qgrad_tensors_device.back()->GetDeviceBuffer());
    }

    if(time_kernel)
    {
        // not need output z matrix
        auto argument_fwd =
            gemm_fwd.MakeArgument(p_q,
                                  p_k,
                                  p_v,
                                  p_y_fwd,
                                  p_z_nullptr,
                                  p_lse_fwd,
                                  {}, // p_acc0_biases
                                  {}, // p_acc1_biases
                                  problem_descs_fwd,
                                  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

        // specify workspace for problem_desc
        DeviceMem problem_desc_workspace_fwd(gemm_fwd.GetWorkSpaceSize(&argument_fwd));

        gemm_fwd.SetWorkSpacePointer(&argument_fwd, problem_desc_workspace_fwd.GetDeviceBuffer());

        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});

        float tflops_fwd = static_cast<float>(flop_fwd) / 1.E9 / ave_time_fwd;

        float gb_per_sec_fwd = num_byte_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(p_q,
                                  p_k,
                                  p_z_nullptr,
                                  p_v,
                                  p_y_bwd,
                                  p_lse_bwd,
                                  p_ygrad,
                                  p_qgrad,
                                  p_kgrad,
                                  p_vgrad,
                                  {}, // std::array<void*, 1> p_acc0_biases;
                                  {}, // std::array<void*, 1> p_acc1_biases;
                                  {},
                                  {},
                                  problem_descs_bwd,
                                  QKVElementOp{},
                                  QKVElementOp{},
                                  Scale{alpha},
                                  QKVElementOp{},
                                  YElementOp{},
                                  p_drop,
                                  std::tuple<unsigned long long, unsigned long long>(seed, offset));

        DeviceMem problem_desc_workspace_bwd(gemm_bwd.GetWorkSpaceSize(&argument_bwd));

        gemm_bwd.SetWorkSpacePointer(&argument_bwd, problem_desc_workspace_bwd.GetDeviceBuffer());

        if(!gemm_bwd.IsSupportedArgument(argument_bwd))
        {
            std::cout << gemm_bwd.GetTypeString() << " does not support this problem" << std::endl;

            return 0;
        }

        float ave_time_bwd = invoker_bwd.Run(argument_bwd, StreamConfig{nullptr, true});

        float tflops_bwd = static_cast<float>(flop_bwd) / 1.E9 / ave_time_bwd;

        float gb_per_sec_bwd = num_byte_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;
    }

    for(std::size_t i = 0; i < group_count; i++)
    {
        qgrad_tensors_device[i]->SetZero();
        kgrad_tensors_device[i]->SetZero();
        vgrad_tensors_device[i]->SetZero();
    }

    bool pass = true;
    if(do_verification)
    {
        // get z matrix
        auto argument_fwd =
            gemm_fwd.MakeArgument(p_q,
                                  p_k,
                                  p_v,
                                  p_y_fwd,
                                  p_z_fwd,
                                  p_lse_fwd,
                                  {}, // p_acc0_biases
                                  {}, // p_acc1_biases
                                  problem_descs_fwd,
                                  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

        // specify workspace for problem_desc
        DeviceMem problem_desc_workspace_fwd_verify(gemm_fwd.GetWorkSpaceSize(&argument_fwd));

        gemm_fwd.SetWorkSpacePointer(&argument_fwd,
                                     problem_desc_workspace_fwd_verify.GetDeviceBuffer());

        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});

        auto argument_bwd =
            gemm_bwd.MakeArgument(p_q,
                                  p_k,
                                  p_z_bwd,
                                  p_v,
                                  p_y_bwd,
                                  p_lse_bwd,
                                  p_ygrad,
                                  p_qgrad,
                                  p_kgrad,
                                  p_vgrad,
                                  {}, // std::array<void*, 1> p_acc0_biases;
                                  {}, // std::array<void*, 1> p_acc1_biases;
                                  {},
                                  {},
                                  problem_descs_bwd,
                                  QKVElementOp{},
                                  QKVElementOp{},
                                  Scale{alpha},
                                  QKVElementOp{},
                                  YElementOp{},
                                  p_drop,
                                  std::tuple<unsigned long long, unsigned long long>(seed, offset));
        DeviceMem problem_desc_workspace_bwd_verify(gemm_bwd.GetWorkSpaceSize(&argument_bwd));
        gemm_bwd.SetWorkSpacePointer(&argument_bwd,
                                     problem_desc_workspace_bwd_verify.GetDeviceBuffer());
        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});

        for(std::size_t i = 0; i < group_count; i++)
        {
            int G1Q = q_tensors[i].GetLengths()[1];
            // copy z matirx data form device
            z_fwd_tensors_device[i]->FromDevice(z_fwd_tensors[i].mData.data());
            z_fwd_tensors[i].ForEach([&](auto& self, auto idx) {
                z_fwd_g_m_ns[i](idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
            });
            z_bwd_tensors_device[i]->FromDevice(z_bwd_tensors[i].mData.data());
            z_bwd_tensors[i].ForEach([&](auto& self, auto idx) {
                z_bwd_g_m_ns[i](idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
            });
            run_attention_fwd_host(q_g_m_ks[i],
                                   k_g_n_ks[i],
                                   v_g_n_os[i],
                                   alpha,
                                   s_g_m_ns[i],
                                   p_g_m_ns[i],
                                   y_g_m_os[i],
                                   lse_g_ms[i],
                                   p_drop_g_m_ns[i],
                                   z_fwd_g_m_ns[i],
                                   p_dropout_in_uint8_t,
                                   rp_dropout);

            int G0         = q_tensors[i].GetLengths()[0];
            int O          = v_tensors[i].GetLengths()[2];
            int N          = v_tensors[i].GetLengths()[3];
            int M          = q_tensors[i].GetLengths()[2];
            int K          = q_tensors[i].GetLengths()[3];
            int BatchCount = G0 * G1Q;
            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});

            ygrad_tensors[i].ForEach([&](auto& self, auto idx) {
                ygrad_g_m_o(idx[0] * G1Q + idx[1], idx[2], idx[3]) = self(idx);
            });
            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 = dY * V^T
            auto v_g_o_n = v_g_n_os[i].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}});
            auto ref_dropout         = ReferenceDropoutInstance{};
            auto ref_dropout_invoker = ref_dropout.MakeInvoker();
            auto ref_dropout_argment = ref_dropout.MakeArgument(
                z_bwd_g_m_ns[i], pgrad_drop_g_m_n, pgrad_g_m_n, p_dropout_in_uint8_t, rp_dropout);
            ref_dropout_invoker.Run(ref_dropout_argment);

            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_os[i](idx_gmo));
                }
                self(idx_gmn) = ck::type_convert<InputDataType>(
                    ck::type_convert<AccDataType>(p_g_m_ns[i](idx_gmn)) *
                    (ck::type_convert<AccDataType>(pgrad_g_m_n(idx_gmn)) - ygrad_dot_y));
            });

            auto p_drop_g_n_m = p_drop_g_m_ns[i].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}});
            ref_gemm1_grad_invoker.Run(RefGemm1GradArg{
                sgrad_g_m_n, k_g_n_ks[i], qgrad_g_m_k, PassThrough{}, PassThrough{}, Scale{alpha}});
            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_ks[i], kgrad_g_n_k, PassThrough{}, PassThrough{}, Scale{alpha}});

            Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_tensors[i].GetLengths(),
                                                              q_tensors[i].GetStrides());
            Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(kgrad_tensors[i].GetLengths(),
                                                              kgrad_tensors[i].GetStrides());
            Tensor<OutputDataType> vgrad_gs_os_ns_host_result(vgrad_tensors[i].GetLengths(),
                                                              vgrad_tensors[i].GetStrides());
            Tensor<InputDataType> y_gs_ms_os_host_result(y_tensors[i].GetLengths(),
                                                         y_tensors[i].GetStrides());
            Tensor<LSEDataType> lse_gs_ms_host_result(lse_tensors[i].GetLengths(),
                                                      lse_tensors[i].GetStrides());

            Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_tensors[i].GetLengths(),
                                                                q_tensors[i].GetStrides());
            Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(kgrad_tensors[i].GetLengths(),
                                                                kgrad_tensors[i].GetStrides());
            Tensor<OutputDataType> vgrad_gs_os_ns_device_result(vgrad_tensors[i].GetLengths(),
                                                                vgrad_tensors[i].GetStrides());
            Tensor<InputDataType> y_gs_ms_os_device_result(y_tensors[i].GetLengths(),
                                                           y_tensors[i].GetStrides());
            Tensor<LSEDataType> lse_gs_ms_device_result(lse_tensors[i].GetLengths(),
                                                        lse_tensors[i].GetStrides());

            qgrad_tensors_device[i]->FromDevice(qgrad_gs_ms_ks_device_result.data());
            kgrad_tensors_device[i]->FromDevice(kgrad_gs_ns_ks_device_result.data());
            vgrad_tensors_device[i]->FromDevice(vgrad_gs_os_ns_device_result.data());
            y_tensors_device[i]->FromDevice(y_gs_ms_os_device_result.data());
            lse_tensors_device[i]->FromDevice(lse_gs_ms_device_result.data());

            // permute
            y_gs_ms_os_host_result.ForEach([&](auto& self, auto idx) {
                const size_t& g0  = idx[0];
                const size_t& g1q = idx[1];

                const size_t g = g0 * G1Q + g1q;

                self(idx) = y_g_m_os[i](g, idx[2], idx[3]);
            });
            lse_gs_ms_host_result.ForEach([&](auto& self, auto idx) {
                const size_t& g0  = idx[0];
                const size_t& g1q = idx[1];

                const size_t g = g0 * G1Q + g1q;

                self(idx) = lse_g_ms[i](g, idx[2]);
            });
            qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
                const size_t& g0  = idx[0];
                const size_t& g1q = idx[1];

                const size_t g = g0 * G1Q + g1q;

                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& g1q = idx[1];

                const size_t g = g0 * G1Q + g1q;

                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& g1q = idx[1];

                const size_t g = g0 * G1Q + g1q;

                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_integer_err(z_fwd_tensors[i].mData, z_bwd_tensors[i].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); }
