// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include <iostream>
#include <sstream>

#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_xdl_cshuffle_v1.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"

namespace ck {
namespace tensor_operation {
namespace device {

template <typename GridwiseGemm,
          typename FloatAB,
          typename FloatC,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename AccElementwiseOperation,
          typename B1ElementwiseOperation,
          typename CElementwiseOperation,
          typename AGridDesc_AK0_M_AK1,
          typename BGridDesc_BK0_N_BK1,
          typename B1GridDesc_BK0_N_BK1,
          typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
          typename Block2CTileMap,
          typename ComputeBasePtrOfStridedBatch,
          bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
        kernel_batched_gemm_softmax_gemm_xdl_cshuffle_v1(
            const FloatAB* __restrict__ p_a_grid,
            const FloatAB* __restrict__ p_b_grid,
            const FloatAB* __restrict__ p_b1_grid,
            FloatC* __restrict__ p_c_grid,
            const AElementwiseOperation a_element_op,
            const BElementwiseOperation b_element_op,
            const AccElementwiseOperation acc_element_op,
            const B1ElementwiseOperation b1_element_op,
            const CElementwiseOperation c_element_op,
            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
            const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
            const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
                c_grid_desc_mblock_mperblock_nblock_nperblock,
            const Block2CTileMap block_2_ctile_map,
            const index_t batch_count,
            const ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
    const index_t num_blocks_per_batch =
        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);

    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(g_idx)));
    const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));

    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
                                                  p_b_grid + b_batch_offset,
                                                  p_b1_grid + b1_batch_offset,
                                                  p_c_grid + c_batch_offset,
                                                  p_shared,
                                                  a_element_op,
                                                  b_element_op,
                                                  acc_element_op,
                                                  b1_element_op,
                                                  c_element_op,
                                                  a_grid_desc_ak0_m_ak1,
                                                  b_grid_desc_bk0_n_bk1,
                                                  b1_grid_desc_bk0_n_bk1,
                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                  block_2_ctile_map);
#else
    ignore = p_a_grid;
    ignore = p_b_grid;
    ignore = p_b1_grid;
    ignore = p_c_grid;
    ignore = a_element_op;
    ignore = b_element_op;
    ignore = acc_element_op;
    ignore = b1_element_op;
    ignore = c_element_op;
    ignore = a_grid_desc_ak0_m_ak1;
    ignore = b_grid_desc_bk0_n_bk1;
    ignore = b1_grid_desc_bk0_n_bk1;
    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
    ignore = block_2_ctile_map;
    ignore = batch_count;
    ignore = compute_base_ptr_of_batch;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
}

// Computes C = A * B0 * B1
//              ^^^^^^ (Acc0)
//              ^^^^^^^^^^^ (Acc1)
template <typename ALayout,
          typename BLayout, // B0Layout
          typename B1Layout,
          typename CPermuteNumDims_G_M_Gemm1N, // Sequence<NumDimG, NumDimM, NumDimGemm1N>
          typename ADataType,
          typename BDataType,
          typename B1DataType,
          typename CDataType,
          typename GemmAccDataType,
          typename CShuffleDataType,
          typename AElementwiseOperation,
          typename BElementwiseOperation,
          typename AccElementwiseOperation,
          typename B1ElementwiseOperation,
          typename CElementwiseOperation,
          GemmSpecialization GemmSpec,
          index_t NumGemmKPrefetchStage,
          index_t BlockSize,
          index_t MPerBlock,
          index_t NPerBlock, // Gemm0NPerBlock
          index_t KPerBlock, // Gemm0KPerBlock
          index_t Gemm1NPerBlock,
          index_t Gemm1KPerBlock,
          index_t AK1,
          index_t BK1,
          index_t B1K1,
          index_t MPerXDL,
          index_t NPerXDL,
          index_t MXdlPerWave,
          index_t NXdlPerWave,
          index_t Gemm1NXdlPerWave,
          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          index_t ABlockTransferSrcVectorDim,
          index_t ABlockTransferSrcScalarPerVector,
          index_t ABlockTransferDstScalarPerVector_AK1,
          bool ABlockLdsExtraM,
          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
          typename BBlockTransferThreadClusterArrangeOrder,
          typename BBlockTransferSrcAccessOrder,
          index_t BBlockTransferSrcVectorDim,
          index_t BBlockTransferSrcScalarPerVector,
          index_t BBlockTransferDstScalarPerVector_BK1,
          bool BBlockLdsExtraN,
          typename B1BlockTransferThreadClusterLengths_BK0_N_BK1,
          typename B1BlockTransferThreadClusterArrangeOrder,
          typename B1BlockTransferSrcAccessOrder,
          index_t B1BlockTransferSrcVectorDim,
          index_t B1BlockTransferSrcScalarPerVector,
          index_t B1BlockTransferDstScalarPerVector_BK1,
          bool B1BlockLdsExtraN,
          index_t CShuffleMXdlPerWavePerShuffle,
          index_t CShuffleNXdlPerWavePerShuffle,
          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
          LoopScheduler LoopSched = LoopScheduler::Default>
struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle
    : public DeviceBatchedGemmSoftmaxGemmPermute<ALayout,
                                                 BLayout,
                                                 B1Layout,
                                                 CPermuteNumDims_G_M_Gemm1N,
                                                 ADataType,
                                                 BDataType,
                                                 B1DataType,
                                                 CDataType,
                                                 AElementwiseOperation,
                                                 BElementwiseOperation,
                                                 AccElementwiseOperation,
                                                 B1ElementwiseOperation,
                                                 CElementwiseOperation>
{
    using DeviceOp = DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle;

    static constexpr auto I0 = Number<0>{};
    static constexpr auto I1 = Number<1>{};
    static constexpr auto I2 = Number<2>{};

    static constexpr auto matrix_padder =
        GemmGemmPadder<GemmSpec, index_t, index_t, index_t, index_t>{
            MPerBlock, NPerBlock, KPerBlock, Gemm1NPerBlock};

    static auto MakeAGridDescriptor_AK0_M_AK1(index_t MRaw, index_t KRaw, index_t StrideA)
    {
        const auto a_grid_desc_mraw_kraw = [&]() {
            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
            {
                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
                                                    make_tuple(StrideA, I1));
            }
            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
            {
                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
                                                    make_tuple(I1, StrideA));
            }
        }();

        const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
        const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;

        const auto MPad = M - MRaw;
        const auto KPad = K - KRaw;

        if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
                     GemmSpec == GemmSpecialization::MNKPadding)
        {
            // pad both M and K
            assert(K % AK1 == 0);

            const auto AK0 = K / AK1;

            const auto a_grid_desc_m_k =
                transform_tensor_descriptor(a_grid_desc_mraw_kraw,
                                            make_tuple(make_right_pad_transform(MRaw, MPad),
                                                       make_right_pad_transform(KRaw, KPad)),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto a_grid_desc_ak0_m_ak1 =
                transform_tensor_descriptor(a_grid_desc_m_k,
                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
                                                       make_pass_through_transform(M)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
                          GemmSpec == GemmSpecialization::MNPadding)
        {
            // pad M, but not K
            assert(KRaw % AK1 == 0);

            const auto AK0 = KRaw / AK1;

            const auto a_grid_desc_ak0_m_ak1 =
                transform_tensor_descriptor(a_grid_desc_mraw_kraw,
                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
                                                       make_right_pad_transform(MRaw, MPad)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
                          GemmSpec == GemmSpecialization::NKPadding)
        {
            // pad K, but not M
            assert(K % AK1 == 0);

            const auto AK0 = K / AK1;

            const auto a_grid_desc_m_k = transform_tensor_descriptor(
                a_grid_desc_mraw_kraw,
                make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(KRaw, KPad)),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto a_grid_desc_ak0_m_ak1 =
                transform_tensor_descriptor(a_grid_desc_m_k,
                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
                                                       make_pass_through_transform(MRaw)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
        else
        {
            // not pad M or K
            assert(KRaw % AK1 == 0);

            const auto AK0 = KRaw / AK1;

            const auto a_grid_desc_ak0_m_ak1 =
                transform_tensor_descriptor(a_grid_desc_mraw_kraw,
                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
                                                       make_pass_through_transform(MRaw)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return a_grid_desc_ak0_m_ak1;
        }
    }

    static auto MakeBGridDescriptor_BK0_N_BK1(index_t KRaw, index_t NRaw, index_t StrideB)
    {
        const auto b_grid_desc_nraw_kraw = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
                                                    make_tuple(I1, StrideB));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
                                                    make_tuple(StrideB, I1));
            }
        }();

        const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
        const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;

        const auto NPad = N - NRaw;
        const auto KPad = K - KRaw;

        if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
                     GemmSpec == GemmSpecialization::MNKPadding)
        {
            // pad both N and K
            const auto BK0 = K / BK1;

            const auto b_grid_desc_n_k =
                transform_tensor_descriptor(b_grid_desc_nraw_kraw,
                                            make_tuple(make_right_pad_transform(NRaw, NPad),
                                                       make_right_pad_transform(KRaw, KPad)),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto b_grid_desc_bk0_n_bk1 =
                transform_tensor_descriptor(b_grid_desc_n_k,
                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
                                                       make_pass_through_transform(N)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
                          GemmSpec == GemmSpecialization::MNPadding)
        {
            // pad N, but not K
            const auto BK0 = KRaw / BK1;

            const auto b_grid_desc_bk0_n_bk1 =
                transform_tensor_descriptor(b_grid_desc_nraw_kraw,
                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
                                                       make_right_pad_transform(NRaw, NPad)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
        else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
                          GemmSpec == GemmSpecialization::MKPadding)
        {
            // pad K, but not N
            const auto BK0 = K / BK1;

            const auto b_grid_desc_n_k = transform_tensor_descriptor(
                b_grid_desc_nraw_kraw,
                make_tuple(make_pass_through_transform(NRaw), make_right_pad_transform(KRaw, KPad)),
                make_tuple(Sequence<0>{}, Sequence<1>{}),
                make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto b_grid_desc_bk0_n_bk1 =
                transform_tensor_descriptor(b_grid_desc_n_k,
                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
                                                       make_pass_through_transform(NRaw)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
        else
        {
            // not pad N or K
            const auto BK0 = KRaw / BK1;

            const auto b_grid_desc_bk0_n_bk1 =
                transform_tensor_descriptor(b_grid_desc_nraw_kraw,
                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
                                                       make_pass_through_transform(NRaw)),
                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b_grid_desc_bk0_n_bk1;
        }
    }

    // Args: Gemm1KRaw, Gemm1NRaw, StrideB1
    static auto MakeB1GridDescriptor_BK0_N_BK1(index_t KRaw, index_t NRaw, index_t StrideB)
    {
        const auto b1_grid_desc_nraw_kraw = [&]() {
            if constexpr(is_same<tensor_layout::gemm::RowMajor, B1Layout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
                                                    make_tuple(I1, StrideB));
            }
            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, B1Layout>::value)
            {
                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
                                                    make_tuple(StrideB, I1));
            }
        }();

        const auto N = math::integer_divide_ceil(NRaw, Gemm1NPerBlock) * Gemm1NPerBlock;
        const auto K = math::integer_divide_ceil(KRaw, Gemm1KPerBlock) * Gemm1KPerBlock;

        const auto NPad = N - NRaw;
        const auto KPad = K - KRaw;

        // TODO: implement finer-grained padding
        if constexpr(GemmSpec == GemmSpecialization::Default)
        {
            const auto B1K0 = KRaw / B1K1;

            const auto b1_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                b1_grid_desc_nraw_kraw,
                make_tuple(make_unmerge_transform(make_tuple(B1K0, B1K1)),
                           make_pass_through_transform(NRaw)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b1_grid_desc_bk0_n_bk1;
        }
        else
        {
            // pad both B1N and B1K
            const auto B1K0 = K / B1K1;

            const auto b1_grid_desc_n_k =
                transform_tensor_descriptor(b1_grid_desc_nraw_kraw,
                                            make_tuple(make_right_pad_transform(NRaw, NPad),
                                                       make_right_pad_transform(KRaw, KPad)),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
                                            make_tuple(Sequence<0>{}, Sequence<1>{}));

            const auto b1_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
                b1_grid_desc_n_k,
                make_tuple(make_unmerge_transform(make_tuple(B1K0, B1K1)),
                           make_pass_through_transform(N)),
                make_tuple(Sequence<1>{}, Sequence<0>{}),
                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));

            return b1_grid_desc_bk0_n_bk1;
        }
    }

    // assume C[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
    static auto MakeCGridDescriptor_M_N(const std::vector<index_t>& c_gs_ms_ns_lengths_vec,
                                        const std::vector<index_t>& c_gs_ms_ns_strides_vec)
    {
        constexpr index_t NumDimG = CPermuteNumDims_G_M_Gemm1N::At(I0);
        constexpr index_t NumDimM = CPermuteNumDims_G_M_Gemm1N::At(I1);
        constexpr index_t NumDimN = CPermuteNumDims_G_M_Gemm1N::At(I2); // NumDimGemm1N

        assert(c_gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
               c_gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN);

        const auto to_tuple = [&](auto& vec, auto start, auto end) {
            return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
        };

        const auto c_ms_ns_lengths = to_tuple(
            c_gs_ms_ns_lengths_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimN>{});
        const auto c_ms_ns_strides = to_tuple(
            c_gs_ms_ns_strides_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimN>{});

        // dimension Ids for M0, M1, ...
        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};

        // dimension Ids for N0, N1, ...
        constexpr auto nDimIds =
            typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimN, 1>::type{};

        // lengths for M0, M1, ...
        const auto mLengths = get_container_subset(c_ms_ns_lengths, mDimIds);

        // lengths for K0, K1, ...
        const auto nLengths = get_container_subset(c_ms_ns_lengths, nDimIds);

        // naive tensor C[M0, M1, M2, ..., N0, N1, N2...]
        const auto c_grid_desc_ms_ns =
            make_naive_tensor_descriptor(c_ms_ns_lengths, c_ms_ns_strides);

        // transformed tensor C[MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 * N2 * ...]
        const auto c_grid_desc_mraw_nraw = transform_tensor_descriptor(
            c_grid_desc_ms_ns,
            make_tuple(make_merge_transform(mLengths), make_merge_transform(nLengths)),
            make_tuple(mDimIds, nDimIds),
            make_tuple(Sequence<0>{}, Sequence<1>{}));

        return matrix_padder.PadCDescriptor_M_N(c_grid_desc_mraw_nraw);
    }

    // assume C[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
    static auto MakeCGridDescriptor_G_M_N(const std::vector<index_t>& c_gs_ms_ns_lengths_vec,
                                          const std::vector<index_t>& c_gs_ms_ns_strides_vec)
    {
        constexpr index_t NumDimG = CPermuteNumDims_G_M_Gemm1N::At(I0);
        constexpr index_t NumDimM = CPermuteNumDims_G_M_Gemm1N::At(I1);
        constexpr index_t NumDimN = CPermuteNumDims_G_M_Gemm1N::At(I2); // NumDimGemm1N

        assert(c_gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
               c_gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN);

        const auto to_tuple = [&](auto& vec, auto start, auto end) {
            return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
        };

        const auto c_gs_ms_ns_lengths =
            to_tuple(c_gs_ms_ns_lengths_vec, Number<0>{}, Number<NumDimG + NumDimM + NumDimN>{});
        const auto c_gs_ms_ns_strides =
            to_tuple(c_gs_ms_ns_strides_vec, Number<0>{}, Number<NumDimG + NumDimM + NumDimN>{});

        // dimension Ids for G0, G1, ...
        constexpr auto gDimIds = typename arithmetic_sequence_gen<0, NumDimG, 1>::type{};

        // dimension Ids for M0, M1, ...
        constexpr auto mDimIds =
            typename arithmetic_sequence_gen<NumDimG, NumDimG + NumDimM, 1>::type{};

        // dimension Ids for N0, N1, ...
        constexpr auto nDimIds = typename arithmetic_sequence_gen<NumDimG + NumDimM,
                                                                  NumDimG + NumDimM + NumDimN,
                                                                  1>::type{};

        // lengths for G0, G1, ...
        const auto gLengths = get_container_subset(c_gs_ms_ns_lengths, gDimIds);

        // lengths for M0, M1, ...
        const auto mLengths = get_container_subset(c_gs_ms_ns_lengths, mDimIds);

        // lengths for K0, K1, ...
        const auto nLengths = get_container_subset(c_gs_ms_ns_lengths, nDimIds);

        // naive tensor C[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
        const auto c_grid_desc_gs_ms_ns =
            make_naive_tensor_descriptor(c_gs_ms_ns_lengths, c_gs_ms_ns_strides);

        // transformed tensor C[G = G0 * G1 * ..., MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 *
        // N2 * ...]
        const auto c_grid_desc_g_mraw_nraw =
            transform_tensor_descriptor(c_grid_desc_gs_ms_ns,
                                        make_tuple(make_merge_transform(gLengths),
                                                   make_merge_transform(mLengths),
                                                   make_merge_transform(nLengths)),
                                        make_tuple(gDimIds, mDimIds, nDimIds),
                                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));

        // this desc is only for calculating batch offset so no padding needed
        return c_grid_desc_g_mraw_nraw;
    }

    using AGridDesc_AK0_M_AK1  = decltype(MakeAGridDescriptor_AK0_M_AK1(1, 1, 1));
    using BGridDesc_BK0_N_BK1  = decltype(MakeBGridDescriptor_BK0_N_BK1(1, 1, 1));
    using B1GridDesc_BK0_N_BK1 = decltype(MakeB1GridDescriptor_BK0_N_BK1(1, 1, 1));
    using CGridDesc_M_N        = decltype(MakeCGridDescriptor_M_N({}, {}));
    using CGridDesc_G_M_N      = decltype(MakeCGridDescriptor_G_M_N({}, {}));

    struct ComputeBasePtrOfStridedBatch
    {
        ComputeBasePtrOfStridedBatch(index_t BatchStrideA,
                                     index_t BatchStrideB,
                                     index_t BatchStrideB1,
                                     CGridDesc_G_M_N c_grid_desc_g_m_n)
            : BatchStrideA_(BatchStrideA),
              BatchStrideB_(BatchStrideB),
              BatchStrideB1_(BatchStrideB1),
              c_grid_desc_g_m_n_(c_grid_desc_g_m_n)
        {
        }

        __host__ __device__ constexpr long_index_t GetABasePtr(index_t g_idx) const
        {
            return g_idx * static_cast<long_index_t>(BatchStrideA_);
        }

        __host__ __device__ constexpr long_index_t GetBBasePtr(index_t g_idx) const
        {
            return g_idx * static_cast<long_index_t>(BatchStrideB_);
        }

        __host__ __device__ constexpr long_index_t GetB1BasePtr(index_t g_idx) const
        {
            return g_idx * static_cast<long_index_t>(BatchStrideB1_);
        }

        __host__ __device__ constexpr long_index_t GetCBasePtr(index_t g_idx) const
        {
            return c_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
        }

        private:
        index_t BatchStrideA_;
        index_t BatchStrideB_;
        index_t BatchStrideB1_;
        CGridDesc_G_M_N c_grid_desc_g_m_n_;
    };

    // GridwiseGemm
    using GridwiseGemm = GridwiseBatchedGemmSoftmaxGemm_Xdl_CShuffle<
        ADataType, // TODO: distinguish A/B datatype
        GemmAccDataType,
        CShuffleDataType,
        CDataType,
        AElementwiseOperation,
        BElementwiseOperation,
        AccElementwiseOperation,
        B1ElementwiseOperation,
        CElementwiseOperation,
        InMemoryDataOperationEnum::Set,
        AGridDesc_AK0_M_AK1,
        BGridDesc_BK0_N_BK1,
        B1GridDesc_BK0_N_BK1,
        CGridDesc_M_N,
        NumGemmKPrefetchStage,
        BlockSize,
        MPerBlock,
        NPerBlock,
        KPerBlock,
        Gemm1NPerBlock,
        Gemm1KPerBlock,
        AK1,
        BK1,
        B1K1,
        MPerXDL,
        NPerXDL,
        MXdlPerWave,
        NXdlPerWave,
        Gemm1NXdlPerWave,
        ABlockTransferThreadClusterLengths_AK0_M_AK1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_AK1,
        true,
        ABlockLdsExtraM,
        BBlockTransferThreadClusterLengths_BK0_N_BK1,
        BBlockTransferThreadClusterArrangeOrder,
        BBlockTransferSrcAccessOrder,
        BBlockTransferSrcVectorDim,
        BBlockTransferSrcScalarPerVector,
        BBlockTransferDstScalarPerVector_BK1,
        true,
        BBlockLdsExtraN,
        B1BlockTransferThreadClusterLengths_BK0_N_BK1,
        B1BlockTransferThreadClusterArrangeOrder,
        B1BlockTransferSrcAccessOrder,
        B1BlockTransferSrcVectorDim,
        B1BlockTransferSrcScalarPerVector,
        B1BlockTransferDstScalarPerVector_BK1,
        false,
        B1BlockLdsExtraN,
        CShuffleMXdlPerWavePerShuffle,
        CShuffleNXdlPerWavePerShuffle,
        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
        CShuffleBlockTransferScalarPerVector_NPerBlock,
        LoopSched>;

    // Argument
    // FIXME: constness
    struct Argument : public BaseArgument
    {
        Argument(const ADataType* p_a_grid,
                 const BDataType* p_b_grid,
                 const B1DataType* p_b1_grid,
                 CDataType* p_c_grid,
                 index_t MRaw,
                 index_t NRaw,
                 index_t KRaw,
                 index_t Gemm1NRaw, // = ORaw
                 index_t Batch,
                 std::vector<index_t> c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
                 std::vector<index_t> c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
                 index_t StrideA,
                 index_t StrideB,
                 index_t StrideB1,
                 index_t BatchStrideA,
                 index_t BatchStrideB,
                 index_t BatchStrideB1,
                 AElementwiseOperation a_element_op,
                 BElementwiseOperation b_element_op,
                 AccElementwiseOperation acc_element_op,
                 B1ElementwiseOperation b1_element_op,
                 CElementwiseOperation c_element_op)
            : p_a_grid_{p_a_grid},
              p_b_grid_{p_b_grid},
              p_b1_grid_{p_b1_grid},
              p_c_grid_{p_c_grid},
              a_grid_desc_ak0_m_ak1_{DeviceOp::MakeAGridDescriptor_AK0_M_AK1(MRaw, KRaw, StrideA)},
              b_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(KRaw, NRaw, StrideB)},
              b1_grid_desc_bk0_n_bk1_{
                  DeviceOp::MakeB1GridDescriptor_BK0_N_BK1(NRaw, Gemm1NRaw, StrideB1)},
              c_grid_desc_m_n_{DeviceOp::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
                                                                 c_gs_ms_gemm1ns_strides)},
              c_grid_desc_g_m_n_{DeviceOp::MakeCGridDescriptor_G_M_N(c_gs_ms_gemm1ns_lengths,
                                                                     c_gs_ms_gemm1ns_strides)},
              c_grid_desc_mblock_mperblock_nblock_nperblock_{},
              block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_)},
              a_element_op_{a_element_op},
              b_element_op_{b_element_op},
              acc_element_op_{acc_element_op},
              b1_element_op_{b1_element_op},
              c_element_op_{c_element_op},
              batch_count_(Batch),
              compute_base_ptr_of_batch_{
                  BatchStrideA, BatchStrideB, BatchStrideB1, c_grid_desc_g_m_n_}
        {
            if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
                                           b_grid_desc_bk0_n_bk1_,
                                           b1_grid_desc_bk0_n_bk1_,
                                           c_grid_desc_m_n_,
                                           block_2_ctile_map_))
            {
                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
                    GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                        c_grid_desc_m_n_);
            }
        }

        //  private:
        const ADataType* p_a_grid_;
        const BDataType* p_b_grid_;
        const B1DataType* p_b1_grid_;
        CDataType* p_c_grid_;
        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
        B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1_;
        CGridDesc_M_N c_grid_desc_m_n_;
        CGridDesc_G_M_N c_grid_desc_g_m_n_;
        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
            c_grid_desc_mblock_mperblock_nblock_nperblock_;
        typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
        AElementwiseOperation a_element_op_;
        BElementwiseOperation b_element_op_;
        AccElementwiseOperation acc_element_op_;
        B1ElementwiseOperation b1_element_op_;
        CElementwiseOperation c_element_op_;
        index_t batch_count_;
        ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch_;
    };

    // Invoker
    struct Invoker : public BaseInvoker
    {
        using Argument = DeviceOp::Argument;

        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
                                            arg.b_grid_desc_bk0_n_bk1_,
                                            arg.b1_grid_desc_bk0_n_bk1_,
                                            arg.c_grid_desc_m_n_,
                                            arg.block_2_ctile_map_))
            {
                throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
            }

            const index_t grid_size =
                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_) * arg.batch_count_;

            // Gemm0_K
            const auto K =
                arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);

            float ave_time = 0;

            auto launch_kernel = [&](auto has_main_k_block_loop_) {
                const auto kernel = kernel_batched_gemm_softmax_gemm_xdl_cshuffle_v1<
                    GridwiseGemm,
                    ADataType, // TODO: distiguish A/B datatype
                    CDataType,
                    AElementwiseOperation,
                    BElementwiseOperation,
                    AccElementwiseOperation,
                    B1ElementwiseOperation,
                    CElementwiseOperation,
                    DeviceOp::AGridDesc_AK0_M_AK1,
                    DeviceOp::BGridDesc_BK0_N_BK1,
                    DeviceOp::B1GridDesc_BK0_N_BK1,
                    typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
                    typename GridwiseGemm::DefaultBlock2CTileMap,
                    ComputeBasePtrOfStridedBatch,
                    has_main_k_block_loop_>;

                return launch_and_time_kernel(stream_config,
                                              kernel,
                                              dim3(grid_size),
                                              dim3(BlockSize),
                                              0,
                                              arg.p_a_grid_,
                                              arg.p_b_grid_,
                                              arg.p_b1_grid_,
                                              arg.p_c_grid_,
                                              arg.a_element_op_,
                                              arg.b_element_op_,
                                              arg.acc_element_op_,
                                              arg.b1_element_op_,
                                              arg.c_element_op_,
                                              arg.a_grid_desc_ak0_m_ak1_,
                                              arg.b_grid_desc_bk0_n_bk1_,
                                              arg.b1_grid_desc_bk0_n_bk1_,
                                              arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
                                              arg.block_2_ctile_map_,
                                              arg.batch_count_,
                                              arg.compute_base_ptr_of_batch_);
            };

            // Gemm1_K is split into Gemm1_K0/K1 where K1 is known at compile time, so we only need
            // to concern Gemm0's loop
            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
            {
                ave_time = launch_kernel(integral_constant<bool, true>{});
            }
            else
            {
                ave_time = launch_kernel(integral_constant<bool, false>{});
            }

            return ave_time;
        }

        // polymorphic
        float Run(const BaseArgument* p_arg,
                  const StreamConfig& stream_config = StreamConfig{}) override
        {
            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
        }
    };

    static constexpr bool IsValidCompilationParameter()
    {
        // TODO: properly implement this check
        return true;
    }

    static bool IsSupportedArgument(const Argument& arg)
    {
        if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
        {
            return false;
        }

        // Check if C permute dimension matches GEMM + GEMM shape
        const index_t c_g       = arg.c_grid_desc_g_m_n_.GetLength(I0);
        const index_t c_m       = arg.c_grid_desc_g_m_n_.GetLength(I1);
        const index_t c_gemm1n  = arg.c_grid_desc_g_m_n_.GetLength(I2);
        const index_t a_m       = arg.a_grid_desc_ak0_m_ak1_.GetLength(I1);
        const index_t b1_gemm1n = arg.b1_grid_desc_bk0_n_bk1_.GetLength(I1);
        if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n))
        {
            return false;
        }

        // TODO: Check A/B0/B1 length & stride and scalar per vector

        return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
                                           arg.b_grid_desc_bk0_n_bk1_,
                                           arg.b1_grid_desc_bk0_n_bk1_,
                                           arg.c_grid_desc_m_n_,
                                           arg.block_2_ctile_map_);
    }

    // polymorphic
    bool IsSupportedArgument(const BaseArgument* p_arg) override
    {
        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
    }

    static auto MakeArgument(const ADataType* p_a,
                             const BDataType* p_b,
                             const B1DataType* p_b1,
                             CDataType* p_c,
                             index_t MRaw,
                             index_t NRaw,
                             index_t KRaw,
                             index_t Gemm1NRaw,
                             index_t Batch,
                             std::vector<index_t> c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
                             std::vector<index_t> c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
                             index_t StrideA,
                             index_t StrideB,
                             index_t StrideB1,
                             index_t BatchStrideA,
                             index_t BatchStrideB,
                             index_t BatchStrideB1,
                             AElementwiseOperation a_element_op,
                             BElementwiseOperation b_element_op,
                             AccElementwiseOperation acc_element_op,
                             B1ElementwiseOperation b1_element_op,
                             CElementwiseOperation c_element_op)
    {
        return Argument{p_a,
                        p_b,
                        p_b1,
                        p_c,
                        MRaw,
                        NRaw,
                        KRaw,
                        Gemm1NRaw,
                        Batch,
                        c_gs_ms_gemm1ns_lengths,
                        c_gs_ms_gemm1ns_strides,
                        StrideA,
                        StrideB,
                        StrideB1,
                        BatchStrideA,
                        BatchStrideB,
                        BatchStrideB1,
                        a_element_op,
                        b_element_op,
                        acc_element_op,
                        b1_element_op,
                        c_element_op};
    }

    static auto MakeInvoker() { return Invoker{}; }

    // polymorphic
    // FIXME: constness
    std::unique_ptr<BaseArgument>
    MakeArgumentPointer(const void* p_a,
                        const void* p_b,
                        const void* p_b1,
                        void* p_c,
                        index_t MRaw,
                        index_t NRaw,
                        index_t KRaw,
                        index_t Gemm1NRaw,
                        index_t Batch,
                        std::vector<index_t> c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
                        std::vector<index_t> c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
                        index_t StrideA,
                        index_t StrideB,
                        index_t StrideB1,
                        index_t BatchStrideA,
                        index_t BatchStrideB,
                        index_t BatchStrideB1,
                        AElementwiseOperation a_element_op,
                        BElementwiseOperation b_element_op,
                        AccElementwiseOperation acc_element_op,
                        B1ElementwiseOperation b1_element_op,
                        CElementwiseOperation c_element_op) override
    {
        return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
                                          static_cast<const BDataType*>(p_b),
                                          static_cast<const B1DataType*>(p_b1),
                                          static_cast<CDataType*>(p_c),
                                          MRaw,
                                          NRaw,
                                          KRaw,
                                          Gemm1NRaw,
                                          Batch,
                                          c_gs_ms_gemm1ns_lengths,
                                          c_gs_ms_gemm1ns_strides,
                                          StrideA,
                                          StrideB,
                                          StrideB1,
                                          BatchStrideA,
                                          BatchStrideB,
                                          BatchStrideB1,
                                          a_element_op,
                                          b_element_op,
                                          acc_element_op,
                                          b1_element_op,
                                          c_element_op);
    }

    // polymorphic
    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
    {
        return std::make_unique<Invoker>(Invoker{});
    }

    // polymorphic
    std::string GetTypeString() const override
    {
        auto str = std::stringstream();

        // clang-format off
        str << "DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle"
            << "<"
            << BlockSize << ", "
            << MPerBlock << ", "
            << NPerBlock << ", "
            << KPerBlock << ", "
            << AK1 << ", "
            << BK1 << ", "
            << MPerBlock << ", "
            << Gemm1NPerBlock << ", "
            << Gemm1KPerBlock << ", "
            << B1K1 << ">";
        // clang-format on

        return str.str();
    }
};

} // namespace device
} // namespace tensor_operation
} // namespace ck
