// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
/*
Backprop for Gemm + Softmax + Gemm fused operation, where forward prop is defined as:

  Y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o

Computation graph:

          K^T                   V
          |                     |
          |                     |
    Q --- * ----- Softmax ----- * --> Y
              S             P

Kernel inputs:

    Q, K, V, Y, dY, per-row softmax stats (LSE)

Kernel outputs:

    dQ, dK, dV

*/

#define PRINT_HOST 0
#define USING_MASK 0
#define DIM 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_batched_mha_bwd_xdl_cshuffle_qloop_v1.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_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/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 = F16;
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,      CShuffleBlockTransferScalarPerVector_NPerBlock = 4
// When OutputDataType == F16/BF16, 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::MaskUpperTriangleFromBottomRight;
#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 DeviceGemmInstance =
    // ########################################################################################|  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| 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|    |    |     |  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|    |    |     |     |     |   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::DeviceBatchedMultiheadAttentionBackward_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,   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 DeviceGemmInstance =
    // ########################################################################################|  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| 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|    |    |     |  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|    |    |     |     |     |   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::DeviceBatchedMultiheadAttentionBackward_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,   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>;
    // ########################################################################################|  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| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds|  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|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|  NXdl|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  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|           |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                               |            |              |
    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|  Wave|                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                 |                |                |                |                |                |           |            |            |                             |                                               |            |              |
    // ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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 DeviceGemmInstance =
    // ########################################################################################|  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| 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|    |    |     |  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|    |    |     |     |     |   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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   8,   8,    2,   32,   32,     2,     1,     4,     1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;               
    // ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   8,   8,    2,   32,   32,     2,     1,     4,     1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;               
    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_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,   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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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::DeviceBatchedMultiheadAttentionBackward_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,   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,      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 TensorD,
          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 TensorD& d_g_m_n,
                            const TensorV& v_g_n_o,
                            const float alpha,
                            TensorS& s_g_m_n,
                            TensorP& p_g_m_n,
                            TensorY& y_g_m_o,
                            TensorLSE& lse_g_m,
                            TensorP& p_drop_g_m_n,
                            TensorZ& z_g_m_n,
                            ZDataType p_dropout_in_16bits,
                            float rp_dropout)
{
    // S = alpha * Q * K^T
    auto k_g_k_n            = k_g_n_k.Transpose({0, 2, 1});
    auto ref_gemm0          = ReferenceGemm0Instance{};
    auto ref_gemm0_invoker  = ref_gemm0.MakeInvoker();
    auto ref_gemm0_argument = ref_gemm0.MakeArgument(
        q_g_m_k, k_g_k_n, s_g_m_n, PassThrough{}, PassThrough{}, Scale{alpha});

    ref_gemm0_invoker.Run(ref_gemm0_argument);

    // bias
    s_g_m_n.ForEach(
        [&](auto& self, auto idx) { self(idx) += ck::type_convert<AccDataType>(d_g_m_n(idx)); });
    // masking
    auto M          = s_g_m_n.GetLengths()[1];
    auto N          = s_g_m_n.GetLengths()[2];
    const auto mask = DeviceGemmInstance::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_16bits, rp_dropout);
    ref_dropout_invoker.Run(ref_dropout_argment);

    // Y = P_dropout * V
    auto ref_gemm1          = ReferenceGemm1Instance{};
    auto ref_gemm1_invoker  = ref_gemm1.MakeInvoker();
    auto ref_gemm1_argument = ref_gemm1.MakeArgument(
        p_drop_g_m_n, v_g_n_o, y_g_m_o, PassThrough{}, PassThrough{}, PassThrough{});

    ref_gemm1_invoker.Run(ref_gemm1_argument);
}

int run(int argc, char* argv[])
{
    bool do_verification = true;
    int init_method      = 1; // method 1 will have slightly higher error; TODO: to investigate
    bool time_kernel     = true;

    // Overall QKV matrices shape
    // y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
    // y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
    // y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
    ck::index_t M  = 512;
    ck::index_t N  = 512;
    ck::index_t K  = DIM;
    ck::index_t O  = DIM;
    ck::index_t G0 = 4;
    ck::index_t G1 = 6;

    bool input_permute  = false;
    bool output_permute = false;

    float p_drop                    = 0.0;
    const unsigned long long seed   = 1;
    const unsigned long long offset = 0;

    if(argc == 1)
    {
        // use default case
    }
    else if(argc == 4)
    {
        do_verification = std::stoi(argv[1]);
        init_method     = std::stoi(argv[2]);
        time_kernel     = std::stoi(argv[3]);
    }
    else if(argc == 13)
    {
        do_verification = std::stoi(argv[1]);
        init_method     = std::stoi(argv[2]);
        time_kernel     = std::stoi(argv[3]);

        M  = std::stoi(argv[4]);
        N  = std::stoi(argv[5]);
        K  = std::stoi(argv[6]);
        O  = std::stoi(argv[7]);
        G0 = std::stoi(argv[8]);
        G1 = std::stoi(argv[9]);

        p_drop = std::stof(argv[10]);

        input_permute  = std::stoi(argv[11]);
        output_permute = std::stoi(argv[12]);
    }
    else
    {
        printf("arg1: verification (0=no, 1=yes)\n");
        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
        printf("arg3: time kernel (0=no, 1=yes)\n");
        printf("arg4 to 11: M, N, K, O, G0, G1\n");
        printf("arg10: scale (alpha)\n");
        printf("arg11 to 12: input / output permute\n");
        exit(0);
    }

    float p_dropout               = 1 - p_drop;
    ZDataType p_dropout_in_16bits = ZDataType(std::floor(p_dropout * 65535.0));
    float rp_dropout              = 1.0 / p_dropout;
    float alpha                   = 1.f / std::sqrt(K);

    std::cout << "do_verification: " << do_verification << std::endl;
    std::cout << "init_method: " << init_method << std::endl;
    std::cout << "time_kernel: " << time_kernel << std::endl;
    std::cout << "M: " << M << std::endl;
    std::cout << "N: " << N << std::endl;
    std::cout << "K: " << K << std::endl;
    std::cout << "O: " << O << std::endl;
    std::cout << "G0: " << G0 << std::endl;
    std::cout << "G1: " << G1 << std::endl;
    std::cout << "alpha: " << alpha << std::endl;
    std::cout << "input_permute: " << input_permute << std::endl;
    std::cout << "output_permute: " << output_permute << std::endl;
    std::cout << "p_drop: " << p_drop << std::endl;
    std::cout << "seed: " << seed << std::endl;
    std::cout << "offset: " << offset << std::endl;

    const ck::index_t BatchCount = G0 * G1;

    std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
    std::vector<ck::index_t> q_gs_ms_ks_strides =
        input_permute
            ? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
            : std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]

    std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
    std::vector<ck::index_t> k_gs_ns_ks_strides =
        input_permute
            ? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
            : std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]

    std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
    std::vector<ck::index_t> v_gs_os_ns_strides =
        input_permute
            ? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
            : std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]

    std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
    std::vector<ck::index_t> y_gs_ms_os_strides =
        output_permute
            ? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
            : std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]

    std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M, N};
    std::vector<ck::index_t> d_gs_ms_ns_strides =
        input_permute
            ? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // D layout [G0, M, G1, N]
            : std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // D layout [G0, G1, M, N]

    std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
    std::vector<ck::index_t> z_gs_ms_ns_strides =
        input_permute
            ? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
            : std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
    // The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward pass
    // Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
    //    = exp(Si) / exp(log(sum(exp() + ...)))
    //    = exp(Si - log(sum(exp() + ...)))
    //               ^^^^^^^^^^^^^^^^^^^^^
    //                       LSE
    std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
    std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]

    Tensor<InputDataType> q_gs_ms_ks(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
    Tensor<InputDataType> k_gs_ns_ks(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
    Tensor<Acc0BiasDataType> d_gs_ms_ns(d_gs_ms_ns_lengths, d_gs_ms_ns_strides);
    Tensor<ZDataType> z_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);

    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 << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
    std::cout << "z_gs_ms_ns: " << z_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;

    z_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});
        d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<Acc0BiasDataType>{-2, 2});
        // d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
        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});
        d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<Acc0BiasDataType>{-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>{});
        d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
        break;
    case 4:
        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
        d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
        break;
    case 5:
        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
        d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
        // dO dot O = [0; 1; 2; ...]
        break;
    case 6:
        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
        d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
        // assume mnko = 256
        // P = softmax(QK) = 0.0039 * ones
        // O = P V = 0.0039 * ones
        // dP = dO V = [0, 1, 2, ...; 0, 1, 2, ...; ...]
        // dO dot O = [127.5; ...]
        // dS = P * (dP - dO dot O)
        //
        break;
    default:
        q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1});
        k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
        v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<InputDataType>{});
        ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<InputDataType>{1}); // dy[g0,g1, m, o]
        d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<Acc0BiasDataType>{1});
        // assume mnko = 256
        // P = softmax(QK) = 0.0039 * ones
        // O = P V = 0.0039 * ones
        // dP = dO V = ones
        // dS = P * (dP - (dO dot O))
        //    = 0.0039 * ones * (ones - 0.0039*256)
        //    = 0.0039 * ones * (ones - 1)
        //    = 0
    }

    // qkv gradients have the same descriptor as with qkv
    DeviceMem q_device_buf(sizeof(InputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
    DeviceMem k_device_buf(sizeof(InputDataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
    DeviceMem d_device_buf(sizeof(Acc0BiasDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
    DeviceMem z_device_buf(sizeof(ZDataType) * z_gs_ms_ns.mDesc.GetElementSpaceSize());
    DeviceMem v_device_buf(sizeof(InputDataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
    DeviceMem y_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
    DeviceMem lse_device_buf(sizeof(LSEDataType) * lse_gs_ms.mDesc.GetElementSpaceSize());
    DeviceMem qgrad_device_buf(sizeof(OutputDataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
    DeviceMem kgrad_device_buf(sizeof(OutputDataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
    DeviceMem vgrad_device_buf(sizeof(OutputDataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
    DeviceMem ygrad_device_buf(sizeof(InputDataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());

    q_device_buf.ToDevice(q_gs_ms_ks.mData.data());
    k_device_buf.ToDevice(k_gs_ns_ks.mData.data());
    d_device_buf.ToDevice(d_gs_ms_ns.mData.data());
    v_device_buf.ToDevice(v_gs_os_ns.mData.data());
    ygrad_device_buf.ToDevice(ygrad_gs_ms_os.mData.data());

    auto gemm    = DeviceGemmInstance{};
    auto invoker = gemm.MakeInvoker();
    // get z matrix
    {
        auto argument = gemm.MakeArgument(
            static_cast<InputDataType*>(q_device_buf.GetDeviceBuffer()),
            static_cast<InputDataType*>(k_device_buf.GetDeviceBuffer()),
            static_cast<ZDataType*>(z_device_buf.GetDeviceBuffer()),
            static_cast<InputDataType*>(v_device_buf.GetDeviceBuffer()),
            static_cast<InputDataType*>(y_device_buf.GetDeviceBuffer()),
            static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
            static_cast<InputDataType*>(ygrad_device_buf.GetDeviceBuffer()),
            static_cast<OutputDataType*>(qgrad_device_buf.GetDeviceBuffer()),
            static_cast<OutputDataType*>(kgrad_device_buf.GetDeviceBuffer()),
            static_cast<OutputDataType*>(vgrad_device_buf.GetDeviceBuffer()),
            static_cast<Acc0BiasDataType*>(d_device_buf.GetDeviceBuffer()), //  p_acc0_bias;
            nullptr,                                                        //  p_acc1_bias;
            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,
            d_gs_ms_ns_lengths, // acc0_bias_gs_ms_ns_lengths
            d_gs_ms_ns_strides, // acc0_bias_gs_ms_ns_strides
            {},                 // acc1_bias_gs_ms_os_lengths,
            {},                 // acc1_bias_gs_ms_os_strides,
            QKVElementOp{},
            QKVElementOp{},
            Scale{alpha},
            QKVElementOp{},
            YElementOp{},
            p_drop,
            std::tuple<unsigned long long, unsigned long long>(seed, offset));

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

            return 0;
        }
        invoker.Run(argument, StreamConfig{nullptr, false});
    }
    // not need output z matrix
    auto argument = gemm.MakeArgument(
        static_cast<InputDataType*>(q_device_buf.GetDeviceBuffer()),
        static_cast<InputDataType*>(k_device_buf.GetDeviceBuffer()),
        static_cast<ZDataType*>(nullptr), // set to nullptr
        static_cast<InputDataType*>(v_device_buf.GetDeviceBuffer()),
        static_cast<InputDataType*>(y_device_buf.GetDeviceBuffer()),
        static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
        static_cast<InputDataType*>(ygrad_device_buf.GetDeviceBuffer()),
        static_cast<OutputDataType*>(qgrad_device_buf.GetDeviceBuffer()),
        static_cast<OutputDataType*>(kgrad_device_buf.GetDeviceBuffer()),
        static_cast<OutputDataType*>(vgrad_device_buf.GetDeviceBuffer()),
        static_cast<Acc0BiasDataType*>(d_device_buf.GetDeviceBuffer()), // p_acc0_bias;
        nullptr,                                                        // p_acc1_bias;
        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,
        d_gs_ms_ns_lengths, // acc0_bias_gs_ms_ns_lengths
        d_gs_ms_ns_strides, // acc0_bias_gs_ms_ns_strides
        {},                 // acc1_bias_gs_ms_os_lengths,
        {},                 // acc1_bias_gs_ms_os_strides,
        QKVElementOp{},
        QKVElementOp{},
        Scale{alpha},
        QKVElementOp{},
        YElementOp{},
        p_drop,
        std::tuple<unsigned long long, unsigned long long>(seed, offset));
    qgrad_device_buf.SetZero();
    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});

    // 5 GEMM ops in total:
    // S_MNK / dP_MNO Gemm (Gemm0 rcr)
    // dQ_MKN Gemm (Gemm1 rrr)
    // dV_NOM / dK_NKM Gemm (Gemm2 crr)
    // 3x MNK + 2x MNO
    std::size_t flop = (size_t(3) * M * N * K + size_t(2) * M * N * O) * 2 * BatchCount;
    // Q/K/V/Y, dQ/dK/dV/dY, LSE
    std::size_t num_btype =
        (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 + sizeof(Acc0BiasDataType) * M * N) *
            BatchCount +
        sizeof(LSEDataType) * M * BatchCount;

    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;

    float gb_per_sec = num_btype / 1.E6 / ave_time;

    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
              << gemm.GetTypeString() << std::endl;

    //       std::cout << "z_g_m_n ref:\n" << z_g_m_n;
    bool pass = true;
    if(do_verification)
    {
        // copy z matirx data form device

        Tensor<InputDataType> q_g_m_k({BatchCount, M, K});
        Tensor<InputDataType> k_g_n_k({BatchCount, N, K});
        Tensor<Acc0BiasDataType> d_g_m_n({G0 * G1, M, N});
        Tensor<ZDataType> z_g_m_n({BatchCount, M, N});
        Tensor<InputDataType> v_g_n_o({BatchCount, N, O});
        Tensor<AccDataType> s_g_m_n({BatchCount, M, N});
        Tensor<InputDataType> p_g_m_n({BatchCount, M, N});
        Tensor<InputDataType> p_drop_g_m_n({BatchCount, M, N});
        Tensor<InputDataType> y_g_m_o({BatchCount, M, O});
        Tensor<LSEDataType> lse_g_m({BatchCount, M});

        z_device_buf.FromDevice(z_gs_ms_ns.mData.data());
        z_gs_ms_ns.ForEach([&](auto& self, auto idx) {
            z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
        });
        q_gs_ms_ks.ForEach([&](auto& self, auto idx) {
            q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
        });
        k_gs_ns_ks.ForEach([&](auto& self, auto idx) {
            k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
        });
        v_gs_os_ns.ForEach([&](auto& self, auto idx) {
            v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx);
        });
        d_gs_ms_ns.ForEach([&](auto& self, auto idx) {
            d_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
        });
        // run fwd again for y, cause z_g_m_n update
        run_attention_fwd_host(q_g_m_k,
                               k_g_n_k,
                               d_g_m_n,
                               v_g_n_o,
                               alpha,
                               s_g_m_n,
                               p_g_m_n,
                               y_g_m_o,
                               lse_g_m,
                               p_drop_g_m_n,
                               z_g_m_n,
                               p_dropout_in_16bits,
                               rp_dropout);
        y_gs_ms_os.ForEach([&](auto& self, auto idx) {
            self(idx) = y_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]);
        });
        lse_gs_ms.ForEach(
            [&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1 + idx[1], idx[2]); });
        y_device_buf.ToDevice(y_gs_ms_os.mData.data());
        lse_device_buf.ToDevice(lse_gs_ms.mData.data());

        // call kernel again
        qgrad_device_buf.SetZero();
        invoker.Run(argument, StreamConfig{nullptr, false});

        Tensor<OutputDataType> qgrad_g_m_k({BatchCount, M, K});
        Tensor<OutputDataType> kgrad_g_n_k({BatchCount, N, K});
        Tensor<OutputDataType> vgrad_g_n_o({BatchCount, N, O});
        Tensor<InputDataType> sgrad_g_m_n({BatchCount, M, N});
        Tensor<InputDataType> pgrad_g_m_n({BatchCount, M, N});
        Tensor<InputDataType> pgrad_drop_g_m_n({BatchCount, M, N});
        Tensor<InputDataType> ygrad_g_m_o({BatchCount, M, O});
        Tensor<InputDataType> ygrad_dot_y_g_m({BatchCount, M});

        ygrad_gs_ms_os.ForEach([&](auto& self, auto idx) {
            ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
        });

#if PRINT_HOST
        {
            std::cout << "q_g_m_k ref:\n" << q_g_m_k;
            std::cout << "k_g_n_k ref:\n" << k_g_n_k;
            std::cout << "v_g_n_o ref:\n" << v_g_n_o;
            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
        }
#endif

        // Gradients
        auto ref_gemm0_grad         = ReferenceGemm0GradInstance{};
        auto ref_gemm0_grad_invoker = ref_gemm0_grad.MakeInvoker();
        using RefGemm0GradArg       = ReferenceGemm0GradInstance::Argument;
        auto ref_gemm1_grad         = ReferenceGemm1GradInstance{};
        auto ref_gemm1_grad_invoker = ref_gemm1_grad.MakeInvoker();
        using RefGemm1GradArg       = ReferenceGemm1GradInstance::Argument;

        // dP_dropout = dY * V^T
        auto v_g_o_n = v_g_n_o.Transpose({0, 2, 1});
        ref_gemm0_grad_invoker.Run(RefGemm0GradArg{
            ygrad_g_m_o, v_g_o_n, pgrad_drop_g_m_n, PassThrough{}, PassThrough{}, Scale{1.f}});
#if PRINT_HOST
        {
            std::cout << "===== dP = dY * V^T\n";
            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
            std::cout << "v_g_o_n ref:\n" << v_g_o_n;
            std::cout << "pgrad_drop_g_m_n ref:\n" << pgrad_drop_g_m_n;
        }
#endif
        // dP = dP_dropout x Z
        auto ref_dropout         = ReferenceDropoutInstance{};
        auto ref_dropout_invoker = ref_dropout.MakeInvoker();
        auto ref_dropout_argment = ref_dropout.MakeArgument(
            z_g_m_n, pgrad_drop_g_m_n, pgrad_g_m_n, p_dropout_in_16bits, rp_dropout);
        ref_dropout_invoker.Run(ref_dropout_argment);

        // dS_i_j = P_i_j .* (dP_i_j - dY_i dot Y_i)
        sgrad_g_m_n.ForEach([&](auto& self, auto idx_gmn) {
            float ygrad_dot_y = 0;
            for(int o = 0; o < O; o++)
            {
                auto idx_gmo = idx_gmn;
                idx_gmo[2]   = o;
                ygrad_dot_y += ck::type_convert<AccDataType>(ygrad_g_m_o(idx_gmo)) *
                               ck::type_convert<AccDataType>(y_g_m_o(idx_gmo));
            }
            self(idx_gmn) = ck::type_convert<InputDataType>(
                ck::type_convert<AccDataType>(p_g_m_n(idx_gmn)) *
                (ck::type_convert<AccDataType>(pgrad_g_m_n(idx_gmn)) - ygrad_dot_y));
        });
#if PRINT_HOST
        {
            std::cout << "===== dS_i_j = P_i_j .* (dP_i_j - dY_i dot Y_i)\n";
            std::cout << "p_g_m_n ref:\n" << p_g_m_n;
            std::cout << "pgrad_g_m_n ref:\n" << pgrad_g_m_n;
            std::cout << "y_g_m_o ref:\n" << y_g_m_o;
            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
            std::cout << "sgrad_g_m_n ref:\n" << sgrad_g_m_n;
        }
#endif
        // dV = P_drop^T * dY
        auto p_drop_g_n_m = p_drop_g_m_n.Transpose({0, 2, 1});
        ref_gemm1_grad_invoker.Run(RefGemm1GradArg{
            p_drop_g_n_m, ygrad_g_m_o, vgrad_g_n_o, PassThrough{}, PassThrough{}, Scale{1.0f}});
#if PRINT_HOST
        {
            std::cout << "===== dV = P^T * dY\n";
            std::cout << "p_drop_g_n_m ref:\n" << p_drop_g_n_m;
            std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
            std::cout << "vgrad_g_n_o ref:\n" << vgrad_g_n_o;
        }
#endif

        // dQ = alpha * dS * K
        ref_gemm1_grad_invoker.Run(RefGemm1GradArg{
            sgrad_g_m_n, k_g_n_k, qgrad_g_m_k, PassThrough{}, PassThrough{}, Scale{alpha}});
#if PRINT_HOST
        {
            std::cout << "===== dQ = alpha * dS * K\n";
            std::cout << "sgrad_g_m_n ref:\n" << sgrad_g_m_n;
            std::cout << "k_g_n_k ref:\n" << k_g_n_k;
            std::cout << "qgrad_g_m_k ref:\n" << qgrad_g_m_k;
        }
#endif

        // dK = alpha * dS^T * Q
        auto sgrad_g_n_m = sgrad_g_m_n.Transpose({0, 2, 1});
        ref_gemm1_grad_invoker.Run(RefGemm1GradArg{
            sgrad_g_n_m, q_g_m_k, kgrad_g_n_k, PassThrough{}, PassThrough{}, Scale{alpha}});
#if PRINT_HOST
        {
            std::cout << "===== dK = alpha * dS^T * Q\n";
            std::cout << "sgrad_g_n_m ref:\n" << sgrad_g_n_m;
            std::cout << "q_g_m_k ref:\n" << q_g_m_k;
            std::cout << "kgrad_g_n_k ref:\n" << kgrad_g_n_k;
        }
#endif

        Tensor<OutputDataType> qgrad_gs_ms_ks_host_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
        Tensor<OutputDataType> kgrad_gs_ns_ks_host_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
        Tensor<OutputDataType> vgrad_gs_os_ns_host_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);

        Tensor<OutputDataType> qgrad_gs_ms_ks_device_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
        Tensor<OutputDataType> kgrad_gs_ns_ks_device_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
        Tensor<OutputDataType> vgrad_gs_os_ns_device_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);

        qgrad_device_buf.FromDevice(qgrad_gs_ms_ks_device_result.mData.data());
        kgrad_device_buf.FromDevice(kgrad_gs_ns_ks_device_result.mData.data());
        vgrad_device_buf.FromDevice(vgrad_gs_os_ns_device_result.mData.data());

        // permute
        qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
            const size_t& g0 = idx[0];
            const size_t& g1 = idx[1];

            const size_t g = g0 * G1 + g1;

            self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
        });
        kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
            const size_t& g0 = idx[0];
            const size_t& g1 = idx[1];

            const size_t g = g0 * G1 + g1;

            self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
        });
        vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
            const size_t& g0 = idx[0];
            const size_t& g1 = idx[1];

            const size_t g = g0 * G1 + g1;

            self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
        });

        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); }
