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

#pragma once

#include <iostream>
#include <sstream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>

#include "ck/ck.hpp"
#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/device_batched_gemm_softmax_gemm_permute.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_wmma_cshuffle.hpp"
#include "ck/tensor_operation/operator_transform/transform_contraction_to_gemm_arraybase.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"

namespace ck {
namespace tensor_operation {
namespace device {

template <typename DeviceOp,
          typename GridwiseOp,
          typename ADataType,
          typename B0DataType,
          typename B1DataType,
          typename CDataType,
          typename AElementwiseOperation,
          typename B0ElementwiseOperation,
          typename AccElementwiseOperation,
          typename B1ElementwiseOperation,
          typename CElementwiseOperation,
          bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
        kernel_batched_gemm_softmax_gemm_wmma_cshuffle(const ADataType* __restrict__ p_a_grid,
                                                       const B0DataType* __restrict__ p_b0_grid,
                                                       const B1DataType* __restrict__ p_b1_grid,
                                                       CDataType* __restrict__ p_c_grid,
                                                       index_t M,
                                                       index_t N,
                                                       index_t K,
                                                       index_t O,
                                                       index_t G0,
                                                       index_t G1,
                                                       float alpha,
                                                       bool input_permute,
                                                       bool output_permute)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))

    // clang-format off
// ***************************************************
// Make Tensor Descriptors
    constexpr index_t array_size = 4;
    std::array<ck::index_t, array_size> a_gs_ms_ks_lengths{G0, G1, M, K};
    std::array<ck::index_t, array_size> a_gs_ms_ks_strides =
        input_permute
            ? std::array<ck::index_t, array_size>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
            : std::array<ck::index_t, array_size>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]

    std::array<ck::index_t, array_size> b0_gs_ns_ks_lengths{G0, G1, N, K};
    std::array<ck::index_t, array_size> b0_gs_ns_ks_strides =
        input_permute
            ? std::array<ck::index_t, array_size>{N * G1 * K, K, G1 * K, 1} // B0 layout [G0, N, G1, K]
            : std::array<ck::index_t, array_size>{G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]

    std::array<ck::index_t, array_size> b1_gs_os_ns_lengths{G0, G1, O, N};
    std::array<ck::index_t, array_size> b1_gs_os_ns_strides =
        input_permute
            ? std::array<ck::index_t, array_size>{N * G1 * O, O, 1, G1 * O} // B1 layout [G0, N, G1, O]
            : std::array<ck::index_t, array_size>{G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]

    std::array<ck::index_t, array_size> c_gs_ms_os_lengths{G0, G1, M, O};
    std::array<ck::index_t, array_size> c_gs_ms_os_strides =
        output_permute
            ? std::array<ck::index_t, array_size>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
            : std::array<ck::index_t, array_size>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]

    const auto a_element_op    = AElementwiseOperation{};
    const auto b0_element_op   = B0ElementwiseOperation{};
    const auto acc0_element_op = AccElementwiseOperation{alpha};
    const auto b1_element_op   = B1ElementwiseOperation{};
    const auto c_element_op    = CElementwiseOperation{};
    // fail to reuse DeviceOp::MakeArgument() because of the __device__ function required.

    const auto a_grid_desc = DeviceOp::MakeAGridDescriptor(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
    const auto b0_grid_desc =
        DeviceOp::MakeB0GridDescriptor(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
    const auto b1_grid_desc =
        DeviceOp::MakeB1GridDescriptor(b1_gs_os_ns_lengths, b1_gs_os_ns_strides);
    const auto c_grid_desc_m_n =
                  DeviceOp::Transform::MakeCGridDescriptor_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);
    const auto c_grid_desc_mblock_mperblock_nblock_nperblock = 
                  GridwiseOp::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
    const auto block_2_ctile_map = GridwiseOp::MakeDefaultBlock2CTileMap(c_grid_desc_m_n, 1, 1);

    const auto a_grid_desc_g_m_k =
                  DeviceOp::Transform::MakeAGridDescriptor_G_M_K(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
    const auto b0_grid_desc_g_l_k = 
                  DeviceOp::Transform::MakeB0GridDescriptor_G_N_K(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
    const auto b1_grid_desc_g_n_l = 
                  DeviceOp::Transform::MakeB1GridDescriptor_G_N_K(b1_gs_os_ns_lengths, b1_gs_os_ns_strides);
    const auto c_grid_desc_g_m_n =
                  DeviceOp::Transform::MakeCGridDescriptor_G_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);
    const auto compute_base_ptr_of_batch = 
                  typename DeviceOp::ComputeBasePtrOfStridedBatch{a_grid_desc_g_m_k, b0_grid_desc_g_l_k, b1_grid_desc_g_n_l, c_grid_desc_g_m_n};
    index_t batch_count = c_grid_desc_g_m_n.GetLength(Number<0>{});
    const auto c0_matrix_mask = typename DeviceOp::C0MatrixMask{b0_grid_desc_g_l_k.GetLength(Number<1>{})};

    // clang-format on
    __shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
    const index_t num_blocks_per_batch =
        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);

    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
    const long_index_t b0_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB0BasePtr(g_idx)));
    const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));

    GridwiseOp::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
                                                p_b0_grid + b0_batch_offset,
                                                p_b1_grid + b1_batch_offset,
                                                p_c_grid + c_batch_offset,
                                                p_shared,
                                                a_grid_desc,
                                                b0_grid_desc,
                                                b1_grid_desc,
                                                c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                a_element_op,
                                                b0_element_op,
                                                acc0_element_op,
                                                b1_element_op,
                                                c_element_op,
                                                c0_matrix_mask,
                                                block_2_ctile_map);
#else
    ignore = p_a_grid;
    ignore = p_b0_grid;
    ignore = p_b1_grid;
    ignore = p_c_grid;
    ignore = M;
    ignore = N;
    ignore = K;
    ignore = O;
    ignore = G0;
    ignore = G1;
    ignore = input_permute;
    ignore = output_permute;
#endif // end of if (defined(__gfx11__))
}

// Self-Attention
template <typename DeviceOp,
          typename GridwiseOp,
          typename QKVDataType,
          typename ODataType,
          typename AElementwiseOperation,
          typename B0ElementwiseOperation,
          typename AccElementwiseOperation,
          typename B1ElementwiseOperation,
          typename CElementwiseOperation,
          bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
        kernel_wmma_self_attention_forward(const QKVDataType* __restrict__ p_qkv_grid,
                                           ODataType* __restrict__ p_out_grid,
                                           index_t batch_size,
                                           index_t sequence_length,
                                           index_t head_count,
                                           index_t head_size,
                                           float alpha)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))

    // clang-format off
// ***************************************************
// Make Tensor Descriptors
// o	Self-attention(packed QKV): [batchSize, sequenceLength, headCount, 3, headSize]
    constexpr index_t array_size = 4;
    std::array<ck::index_t, array_size> qk_gs_ms_ks_lengths{batch_size, head_count, sequence_length, head_size};
    std::array<ck::index_t, array_size> qk_gs_ms_ks_strides{sequence_length * head_count * 3 * head_size, 3 * head_size, head_count * 3 * head_size, 1};
    
    std::array<ck::index_t, array_size> v_gs_os_ns_lengths{batch_size, head_count, head_size, sequence_length};
    std::array<ck::index_t, array_size> v_gs_os_ns_strides{sequence_length * head_count * 3 * head_size, 3 * head_size, 1, head_count * 3 * head_size};

    std::array<ck::index_t, array_size> c_gs_ms_os_lengths{batch_size, head_count, sequence_length, head_size};
    std::array<ck::index_t, array_size> c_gs_ms_os_strides{sequence_length * head_count * head_size, head_size, head_count * head_size,  1};
    

    const auto a_element_op    = AElementwiseOperation{};
    const auto b0_element_op   = B0ElementwiseOperation{};
    const auto acc0_element_op = AccElementwiseOperation{alpha};
    const auto b1_element_op   = B1ElementwiseOperation{};
    const auto c_element_op    = CElementwiseOperation{};

    const auto a_grid_desc = DeviceOp::MakeAGridDescriptor(qk_gs_ms_ks_lengths, qk_gs_ms_ks_strides);
    const auto b0_grid_desc =
        DeviceOp::MakeB0GridDescriptor(qk_gs_ms_ks_lengths, qk_gs_ms_ks_strides);
    const auto b1_grid_desc =
        DeviceOp::MakeB1GridDescriptor(v_gs_os_ns_lengths, v_gs_os_ns_strides);
    const auto c_grid_desc_m_n =
                  DeviceOp::Transform::MakeCGridDescriptor_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);
    const auto c_grid_desc_mblock_mperblock_nblock_nperblock = 
                  GridwiseOp::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
    const auto block_2_ctile_map = GridwiseOp::MakeDefaultBlock2CTileMap(c_grid_desc_m_n, 1, 1);

    const auto a_grid_desc_g_m_k =
                  DeviceOp::Transform::MakeAGridDescriptor_G_M_K(qk_gs_ms_ks_lengths, qk_gs_ms_ks_strides);
    const auto b0_grid_desc_g_l_k = 
                  DeviceOp::Transform::MakeB0GridDescriptor_G_N_K(qk_gs_ms_ks_lengths, qk_gs_ms_ks_strides);
    const auto b1_grid_desc_g_n_l = 
                  DeviceOp::Transform::MakeB1GridDescriptor_G_N_K(v_gs_os_ns_lengths, v_gs_os_ns_strides);
    const auto c_grid_desc_g_m_n =
                  DeviceOp::Transform::MakeCGridDescriptor_G_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);
    const auto compute_base_ptr_of_batch = 
                  typename DeviceOp::ComputeBasePtrOfStridedBatch{a_grid_desc_g_m_k, b0_grid_desc_g_l_k, b1_grid_desc_g_n_l, c_grid_desc_g_m_n};
    index_t batch_count = c_grid_desc_g_m_n.GetLength(Number<0>{});
    const auto c0_matrix_mask = typename DeviceOp::C0MatrixMask{b0_grid_desc_g_l_k.GetLength(Number<1>{})};

    // clang-format on
    __shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
    const index_t num_blocks_per_batch =
        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);

    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
    const long_index_t b0_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB0BasePtr(g_idx)));
    const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));

    const index_t qkv_gap = __builtin_amdgcn_readfirstlane(head_size);
#ifdef CK_SELF_ATTN_DEBUG
    if(get_thread_global_1d_id() == 0)
    {
        printf("batch_size: %d\n", batch_size);
        printf("sequence_length: %d\n", sequence_length);
        printf("head_count: %d\n", head_count);
        printf("head_size: %d\n", head_size);
        printf("qkv_gap: %d\n", qkv_gap);
        printf("get_grid_size(): %d\n", get_grid_size());
        printf("batch_count: %d\n", batch_count);
        printf("blockid: %d\n", get_block_1d_id());
        printf("num_blocks_per_batch: %d\n", num_blocks_per_batch);
        printf("g_idx: %d\n", g_idx);
        printf("a_batch_offset: %ld\n", a_batch_offset);
        printf("b0_batch_offset: %ld\n", b0_batch_offset);
        printf("b1_batch_offset: %ld\n", b1_batch_offset);
    }
#endif
    GridwiseOp::template Run<HasMainKBlockLoop>(p_qkv_grid + 0 * qkv_gap + a_batch_offset,
                                                p_qkv_grid + 1 * qkv_gap + b0_batch_offset,
                                                p_qkv_grid + 2 * qkv_gap + b1_batch_offset,
                                                p_out_grid + c_batch_offset,
                                                p_shared,
                                                a_grid_desc,
                                                b0_grid_desc,
                                                b1_grid_desc,
                                                c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                a_element_op,
                                                b0_element_op,
                                                acc0_element_op,
                                                b1_element_op,
                                                c_element_op,
                                                c0_matrix_mask,
                                                block_2_ctile_map);
#else
    ignore = p_qkv_grid;
    ignore = p_out_grid;
    ignore = batch_size;
    ignore = sequence_length;
    ignore = head_count;
    ignore = head_size;
    ignore = alpha;
#endif // end of if (defined(__gfx11__))
}
// Cross-Attention
// Self-Attention
template <typename DeviceOp,
          typename GridwiseOp,
          typename QDataType,
          typename KVDataType,
          typename ODataType,
          typename AElementwiseOperation,
          typename B0ElementwiseOperation,
          typename AccElementwiseOperation,
          typename B1ElementwiseOperation,
          typename CElementwiseOperation,
          bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
        kernel_wmma_cross_attention_forward(const QDataType* __restrict__ p_q_grid,
                                            const KVDataType* __restrict__ p_kv_grid,
                                            ODataType* __restrict__ p_out_grid,
                                            index_t batch_size,
                                            index_t q_sequence_length,
                                            index_t kv_sequence_length,
                                            index_t head_count,
                                            index_t head_size,
                                            float alpha)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))

    // clang-format off
// ***************************************************
// Make Tensor Descriptors
// o	Self-attention(packed QKV): [batchSize, sequenceLength, headCount, 3, headSize]
    constexpr index_t array_size = 4;
    std::array<ck::index_t, array_size> q_gs_ms_ks_lengths{batch_size, head_count, q_sequence_length, head_size};
    std::array<ck::index_t, array_size> q_gs_ms_ks_strides{q_sequence_length * head_count * head_size, head_size, head_count * head_size, 1};
    
    std::array<ck::index_t, array_size> k_gs_ms_ks_lengths{batch_size, head_count, kv_sequence_length, head_size};
    std::array<ck::index_t, array_size> k_gs_ms_ks_strides{kv_sequence_length * head_count * 2 * head_size, 2 * head_size, head_count * 2 * head_size, 1};

    std::array<ck::index_t, array_size> v_gs_os_ns_lengths{batch_size, head_count, head_size, kv_sequence_length};
    std::array<ck::index_t, array_size> v_gs_os_ns_strides{kv_sequence_length * head_count * 2 * head_size, 2 * head_size, 1, head_count * 2 * head_size};

    std::array<ck::index_t, array_size> c_gs_ms_os_lengths{batch_size, head_count, q_sequence_length, head_size};
    std::array<ck::index_t, array_size> c_gs_ms_os_strides{q_sequence_length * head_count * head_size, head_size, head_count * head_size,  1};
    

    const auto a_element_op    = AElementwiseOperation{};
    const auto b0_element_op   = B0ElementwiseOperation{};
    const auto acc0_element_op = AccElementwiseOperation{alpha};
    const auto b1_element_op   = B1ElementwiseOperation{};
    const auto c_element_op    = CElementwiseOperation{};

    const auto a_grid_desc = DeviceOp::MakeAGridDescriptor(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
    const auto b0_grid_desc =
        DeviceOp::MakeB0GridDescriptor(k_gs_ms_ks_lengths, k_gs_ms_ks_strides);
    const auto b1_grid_desc =
        DeviceOp::MakeB1GridDescriptor(v_gs_os_ns_lengths, v_gs_os_ns_strides);
    const auto c_grid_desc_m_n =
                  DeviceOp::Transform::MakeCGridDescriptor_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);
    const auto c_grid_desc_mblock_mperblock_nblock_nperblock = 
                  GridwiseOp::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
    const auto block_2_ctile_map = GridwiseOp::MakeDefaultBlock2CTileMap(c_grid_desc_m_n, 1, 1);

    const auto a_grid_desc_g_m_k =
                  DeviceOp::Transform::MakeAGridDescriptor_G_M_K(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
    const auto b0_grid_desc_g_l_k = 
                  DeviceOp::Transform::MakeB0GridDescriptor_G_N_K(k_gs_ms_ks_lengths, k_gs_ms_ks_strides);
    const auto b1_grid_desc_g_n_l = 
                  DeviceOp::Transform::MakeB1GridDescriptor_G_N_K(v_gs_os_ns_lengths, v_gs_os_ns_strides);
    const auto c_grid_desc_g_m_n =
                  DeviceOp::Transform::MakeCGridDescriptor_G_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);
    const auto compute_base_ptr_of_batch = 
                  typename DeviceOp::ComputeBasePtrOfStridedBatch{a_grid_desc_g_m_k, b0_grid_desc_g_l_k, b1_grid_desc_g_n_l, c_grid_desc_g_m_n};
    index_t batch_count = c_grid_desc_g_m_n.GetLength(Number<0>{});
    const auto c0_matrix_mask = typename DeviceOp::C0MatrixMask{b0_grid_desc_g_l_k.GetLength(Number<1>{})};

    // clang-format on
    __shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
    const index_t num_blocks_per_batch =
        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);

    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
    const long_index_t b0_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB0BasePtr(g_idx)));
    const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(g_idx)));
    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
        static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));

    const index_t kv_gap = __builtin_amdgcn_readfirstlane(head_size);
#ifdef CK_SELF_ATTN_DEBUG
    if(get_thread_global_1d_id() == 0)
    {
        printf("batch_size: %d\n", batch_size);
        printf("q_sequence_length: %d\n", q_sequence_length);
        printf("k_sequence_length: %d\n", kv_sequence_length);
        printf("head_count: %d\n", head_count);
        printf("head_size: %d\n", head_size);
        printf("kv_gap: %d\n", kv_gap);
        printf("get_grid_size(): %d\n", get_grid_size());
        printf("batch_count: %d\n", batch_count);
        printf("blockid: %d\n", get_block_1d_id());
        printf("num_blocks_per_batch: %d\n", num_blocks_per_batch);
        printf("g_idx: %d\n", g_idx);
        printf("a_batch_offset: %ld\n", a_batch_offset);
        printf("b0_batch_offset: %ld\n", b0_batch_offset);
        printf("b1_batch_offset: %ld\n", b1_batch_offset);
    }
#endif
    GridwiseOp::template Run<HasMainKBlockLoop>(p_q_grid + a_batch_offset,
                                                p_kv_grid + 0 * kv_gap + b0_batch_offset,
                                                p_kv_grid + 1 * kv_gap + b1_batch_offset,
                                                p_out_grid + c_batch_offset,
                                                p_shared,
                                                a_grid_desc,
                                                b0_grid_desc,
                                                b1_grid_desc,
                                                c_grid_desc_mblock_mperblock_nblock_nperblock,
                                                a_element_op,
                                                b0_element_op,
                                                acc0_element_op,
                                                b1_element_op,
                                                c_element_op,
                                                c0_matrix_mask,
                                                block_2_ctile_map);
#else
    ignore = p_q_grid;
    ignore = p_kv_grid;
    ignore = p_out_grid;
    ignore = batch_size;
    ignore = q_sequence_length;
    ignore = kv_sequence_length;
    ignore = head_count;
    ignore = head_size;
    ignore = alpha;
#endif // end of if (defined(__gfx11__))
}
// Computes C = A  * B0 * B1
//         MN = MK * KL * LN
//              ^^^^^^ (Acc0)
//              ^^^^^^^^^^^ (Acc1)
template <index_t NumDimG,
          index_t NumDimM,
          index_t NumDimL,
          index_t NumDimK,
          index_t NumDimN,
          typename ADataType,
          typename B0DataType,
          typename B1DataType,
          typename CDataType,
          typename Acc0BiasDataType,
          typename Acc0DataType,
          typename Acc1BiasDataType,
          typename Acc1DataType,
          typename CShuffleDataType,
          typename AElementwiseOperation,
          typename B0ElementwiseOperation,
          typename AccElementwiseOperation,
          typename B1ElementwiseOperation,
          typename CElementwiseOperation,
          GemmSpecialization GemmSpec,
          TensorSpecialization ASpec,
          TensorSpecialization B0Spec,
          TensorSpecialization B1Spec,
          TensorSpecialization CSpec,
          ck::index_t NumPrefetch,
          ck::index_t BlockSize,
          ck::index_t MPerBlock,
          ck::index_t LPerBlock,
          ck::index_t KPerBlock,
          ck::index_t AK1,
          ck::index_t BK1,
          ck::index_t NPerBlock,
          ck::index_t LTilePerBlock,
          ck::index_t L1,
          ck::index_t MPerWmma,
          ck::index_t LPerWmma,
          ck::index_t NPerWmma,
          ck::index_t MRepeat,
          ck::index_t LRepeat,
          ck::index_t NRepeat,
          typename ABlockTransferThreadClusterLengths_K0_M_K1,
          typename ABlockTransferThreadClusterArrangeOrder,
          typename ABlockTransferSrcAccessOrder,
          ck::index_t ABlockTransferSrcVectorDim,
          ck::index_t ABlockTransferSrcScalarPerVector,
          ck::index_t ABlockTransferDstScalarPerVector_K1,
          bool ABlockLdsAddExtraM,
          typename B0BlockTransferThreadClusterLengths_K0_L_K1,
          typename B0BlockTransferThreadClusterArrangeOrder,
          typename B0BlockTransferSrcAccessOrder,
          ck::index_t B0BlockTransferSrcVectorDim,
          ck::index_t B0BlockTransferSrcScalarPerVector,
          ck::index_t B0BlockTransferDstScalarPerVector_K1,
          bool B0BlockLdsAddExtraL,
          typename B1BlockTransferThreadClusterLengths_L0_N_L1,
          typename B1BlockTransferThreadClusterArrangeOrder,
          typename B1BlockTransferSrcAccessOrder,
          ck::index_t B1BlockTransferSrcVectorDim,
          ck::index_t B1BlockTransferSrcScalarPerVector,
          ck::index_t B1BlockTransferDstScalarPerVector_L1,
          bool B1BlockLdsAddExtraN,
          index_t CShuffleMRepeatPerShuffle,
          index_t CShuffleNRepeatPerShuffle,
          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
          MaskingSpecialization MaskingSpec,
          ck::LoopScheduler LoopSched     = make_default_loop_scheduler(),
          ck::PipelineVersion PipelineVer = ck::PipelineVersion::v1>
struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
    : public DeviceBatchedGemmSoftmaxGemmPermute<NumDimG,
                                                 NumDimM,
                                                 NumDimL,
                                                 NumDimK,
                                                 NumDimN,
                                                 ADataType,
                                                 B0DataType,
                                                 B1DataType,
                                                 CDataType,
                                                 Acc0BiasDataType,
                                                 Acc1BiasDataType,
                                                 AElementwiseOperation,
                                                 B0ElementwiseOperation,
                                                 AccElementwiseOperation,
                                                 B1ElementwiseOperation,
                                                 CElementwiseOperation,
                                                 MaskingSpec>
{
    static_assert(NumDimG > 0 && NumDimM > 0 && NumDimL > 0 && NumDimK > 0 && NumDimN > 0,
                  "Number of dimension must be greater than 0");

    static constexpr index_t NumAcc0Bias = Acc0BiasDataType::Size();
    static constexpr index_t NumAcc1Bias = Acc1BiasDataType::Size();

    // TODO ANT: implement bias combination
    static_assert(NumAcc0Bias == 0 && NumAcc0Bias == 0, "Bias addition is unimplemented");

    static constexpr index_t NumDimGemm0M = NumDimM;
    static constexpr index_t NumDimGemm0N = NumDimL;
    static constexpr index_t NumDimGemm0K = NumDimK;
    static constexpr index_t NumDimGemm1M = NumDimM;
    static constexpr index_t NumDimGemm1N = NumDimN;
    static constexpr index_t NumDimGemm1K = NumDimL;

    using DeviceOp = DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle;

    static constexpr auto I0 = Number<0>{};
    static constexpr auto I1 = Number<1>{};
    static constexpr auto I2 = Number<2>{};
    static constexpr auto I3 = Number<3>{};
    static constexpr auto I4 = Number<4>{};
    static constexpr auto I5 = Number<5>{};
    static constexpr auto I6 = Number<6>{};

    static constexpr auto WmmaK = 16;

    static constexpr auto MWaves = MPerBlock / (MRepeat * MPerWmma);
    static constexpr auto LWaves = LPerBlock / (LRepeat * LPerWmma);
    static constexpr auto NWaves = NPerBlock / (NRepeat * NPerWmma);

    static constexpr auto AEnableLds_auto  = LWaves == 1 ? false : true;
    static constexpr auto B0EnableLds_auto = MWaves == 1 ? false : true;
    static constexpr auto B1EnableLds_auto = MWaves == 1 ? false : true;

    static constexpr auto AEnableLds_manu  = false;
    static constexpr auto B0EnableLds_manu = true;
    static constexpr auto B1EnableLds_manu = true;

    static constexpr auto AEnableLds  = AEnableLds_auto || AEnableLds_manu || (NumPrefetch > 1);
    static constexpr auto B0EnableLds = B0EnableLds_auto || B0EnableLds_manu || (NumPrefetch > 1);
    static constexpr auto B1EnableLds = B1EnableLds_auto || B1EnableLds_manu || (NumPrefetch > 1);

    using Transform = TransformBatchedContractionContractionToBatchedGemmGemm_Wmma<
        Sequence<NumDimG, NumDimM, NumDimL, NumDimK, NumDimN>,
        Sequence<MPerBlock, LPerBlock, KPerBlock, NPerBlock>,
        GemmSpec,
        ASpec,
        B0Spec,
        B1Spec,
        CSpec>;

    __host__ __device__ static auto MakeAGridDescriptor(
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
    {
        if constexpr(AEnableLds)
        {
            return Transform::MakeAGridDescriptor_AK0_M_AK1(
                Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec),
                Number<AK1>{});
        }
        else
        {
            return Transform::
                MakeAGridDescriptor_AKWmma_MBlockRepeat_MWaves_AK0PerWmma_AKRow_MPerWmma_AK1(
                    Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths_vec,
                                                       a_gs_ms_ks_strides_vec),
                    Number<WmmaK>{},
                    Number<MRepeat>{},
                    Number<MWaves>{},
                    Number<MPerWmma>{},
                    Number<AK1>{});
        }
    }

    __host__ __device__ static auto MakeB0GridDescriptor(
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ls_ks_lengths_vec,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ls_ks_strides_vec)
    {
        if constexpr(B0EnableLds)
        {
            return Transform::MakeB0GridDescriptor_BK0_N_BK1(
                Transform::MakeB0GridDescriptor_N_K(b0_gs_ls_ks_lengths_vec,
                                                    b0_gs_ls_ks_strides_vec),
                Number<BK1>{});
        }
        else
        {
            return Transform::
                MakeB0GridDescriptor_BKWmma_LBlockRepeat_LWaves_BK0PerWmma_BKRow_LPerWmma_BK1(
                    Transform::MakeB0GridDescriptor_N_K(b0_gs_ls_ks_lengths_vec,
                                                        b0_gs_ls_ks_strides_vec),
                    Number<WmmaK>{},
                    Number<LRepeat>{},
                    Number<LWaves>{},
                    Number<LPerWmma>{},
                    Number<BK1>{});
        }
    }

    __host__ __device__ static auto MakeB1GridDescriptor(
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_ns_ls_lengths_vec,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_ns_ls_strides_vec)
    {
        if constexpr(B1EnableLds)
        {
            return Transform::MakeB1GridDescriptor_BK0_N_BK1(
                Transform::MakeB1GridDescriptor_N_K(b1_gs_ns_ls_lengths_vec,
                                                    b1_gs_ns_ls_strides_vec),
                Number<L1>{});
        }
        else
        {
            return Transform::
                MakeB1GridDescriptor_BLWmma_NBlockRepeat_NWaves__BL0PerWmma_BLRow_NPerWmma_BL1(
                    Transform::MakeB1GridDescriptor_N_K(b1_gs_ns_ls_lengths_vec,
                                                        b1_gs_ns_ls_strides_vec),
                    Number<WmmaK>{},
                    Number<NRepeat>{},
                    Number<NWaves>{},
                    Number<NPerWmma>{},
                    Number<L1>{});
        }
    }

    using AGridDesc        = decltype(MakeAGridDescriptor({}, {}));
    using B0GridDesc       = decltype(MakeB0GridDescriptor({}, {}));
    using B1GridDesc       = decltype(MakeB1GridDescriptor({}, {}));
    using CGridDesc_M_N    = decltype(Transform::MakeCGridDescriptor_M_N({}, {}));
    using AGridDesc_G_M_K  = decltype(Transform::MakeAGridDescriptor_G_M_K({}, {}));
    using B0GridDesc_G_L_K = decltype(Transform::MakeB0GridDescriptor_G_N_K({}, {}));
    using B1GridDesc_G_N_L = decltype(Transform::MakeB1GridDescriptor_G_N_K({}, {}));
    using CGridDesc_G_M_N  = decltype(Transform::MakeCGridDescriptor_G_M_N({}, {}));

    __host__ __device__ constexpr static auto make_MaskOutPredicate()
    {
        if constexpr(MaskingSpec == MaskingSpecialization::MaskDisabled)
        {
            return MaskDisabledPredicate{};
        }
        else if constexpr(MaskingSpec == MaskingSpecialization::MaskOutUpperTriangle)
        {
            return MaskOutUpperTrianglePredicate{};
        }
    }
    using C0MatrixMask = C0MatrixMask_impl<decltype(make_MaskOutPredicate())>;

    struct ComputeBasePtrOfStridedBatch
    {
        __host__ __device__ ComputeBasePtrOfStridedBatch(const AGridDesc_G_M_K& a_grid_desc_g_m_k,
                                                         const B0GridDesc_G_L_K& b0_grid_desc_g_l_k,
                                                         const B1GridDesc_G_N_L& b1_grid_desc_g_n_l,
                                                         const CGridDesc_G_M_N& c_grid_desc_g_m_n)
            : a_grid_desc_g_m_k_(a_grid_desc_g_m_k),
              b0_grid_desc_g_l_k_(b0_grid_desc_g_l_k),
              b1_grid_desc_g_n_l_(b1_grid_desc_g_n_l),
              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 a_grid_desc_g_m_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
        }

        __host__ __device__ constexpr long_index_t GetB0BasePtr(index_t g_idx) const
        {
            return b0_grid_desc_g_l_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
        }

        __host__ __device__ constexpr long_index_t GetB1BasePtr(index_t g_idx) const
        {
            return b1_grid_desc_g_n_l_.CalculateOffset(make_multi_index(g_idx, 0, 0));
        }

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

        private:
        AGridDesc_G_M_K a_grid_desc_g_m_k_;
        B0GridDesc_G_L_K b0_grid_desc_g_l_k_;
        B1GridDesc_G_N_L b1_grid_desc_g_n_l_;
        CGridDesc_G_M_N c_grid_desc_g_m_n_;
    };

    // GridwiseOp
    using GridwiseOp = GridwiseBatchedGemmSoftmaxGemm_Wmma<
        // DataType Family
        ADataType,
        B0DataType,
        Acc0DataType,
        B1DataType,
        Acc1DataType,
        CShuffleDataType,
        CDataType,
        // ElementwiseOp Family
        AElementwiseOperation,
        B0ElementwiseOperation,
        AccElementwiseOperation,
        B1ElementwiseOperation,
        CElementwiseOperation,
        InMemoryDataOperationEnum::Set,
        // InMemory Data Descriptor
        AGridDesc,
        B0GridDesc,
        B1GridDesc,
        CGridDesc_M_N,
        // Tiling Family
        MPerBlock,
        LPerBlock,
        KPerBlock,
        AK1,
        BK1,
        NPerBlock,
        LTilePerBlock,
        L1,
        MPerWmma,
        LPerWmma,
        NPerWmma,
        MRepeat,
        LRepeat,
        NRepeat,
        // ThreadCluster Family
        BlockSize,
        ABlockTransferThreadClusterLengths_K0_M_K1,
        ABlockTransferThreadClusterArrangeOrder,
        ABlockTransferSrcAccessOrder,
        ABlockTransferSrcVectorDim,
        ABlockTransferSrcScalarPerVector,
        ABlockTransferDstScalarPerVector_K1,
        true,
        AEnableLds,
        ABlockLdsAddExtraM,
        B0BlockTransferThreadClusterLengths_K0_L_K1,
        B0BlockTransferThreadClusterArrangeOrder,
        B0BlockTransferSrcAccessOrder,
        B0BlockTransferSrcVectorDim,
        B0BlockTransferSrcScalarPerVector,
        B0BlockTransferDstScalarPerVector_K1,
        true,
        B0EnableLds,
        B0BlockLdsAddExtraL,
        B1BlockTransferThreadClusterLengths_L0_N_L1,
        B1BlockTransferThreadClusterArrangeOrder,
        B1BlockTransferSrcAccessOrder,
        B1BlockTransferSrcVectorDim,
        B1BlockTransferSrcScalarPerVector,
        B1BlockTransferDstScalarPerVector_L1,
        false,
        B1EnableLds,
        B1BlockLdsAddExtraN,
        CShuffleMRepeatPerShuffle,
        CShuffleNRepeatPerShuffle,
        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
        CShuffleBlockTransferScalarPerVector_NPerBlock,
        Transform::matrix_padder.PadN,
        MaskingSpec == MaskingSpecialization::MaskOutUpperTriangle,
        NumPrefetch,
        LoopSched,
        PipelineVer>;

    struct RawArg : public BaseArgument
    {
        RawArg(const ADataType* p_a_grid,
               const B0DataType* p_b0_grid,
               const B1DataType* p_b1_grid,
               CDataType* p_c_grid,
               index_t M,
               index_t N,
               index_t K,
               index_t O,
               index_t G0,
               index_t G1,
               float alpha,
               bool input_permute,
               bool output_permute)
            : p_a_grid_{p_a_grid},
              p_b0_grid_{p_b0_grid},
              p_b1_grid_{p_b1_grid},
              p_c_grid_{p_c_grid},
              M_{M},
              N_{N},
              K_{K},
              O_{O},
              G0_{G0},
              G1_{G1},
              alpha_{alpha},
              input_permute_{input_permute},
              output_permute_{output_permute}
        {
        }
        // Pointers
        const ADataType* p_a_grid_;
        const B0DataType* p_b0_grid_;
        const B1DataType* p_b1_grid_;
        CDataType* p_c_grid_;

        // Raw Problem Size
        index_t M_;
        index_t N_;
        index_t K_;
        index_t O_;
        index_t G0_;
        index_t G1_;
        float alpha_;
        bool input_permute_;
        bool output_permute_;
    };

    static auto MakeArgument(const ADataType* p_a,
                             const B0DataType* p_b0,
                             const B1DataType* p_b1,
                             CDataType* p_c,
                             index_t M,
                             index_t N,
                             index_t K,
                             index_t O,
                             index_t G0,
                             index_t G1,
                             float alpha,
                             bool input_permute,
                             bool output_permute)
    {
        return RawArg{
            p_a, p_b0, p_b1, p_c, M, N, K, O, G0, G1, alpha, input_permute, output_permute};
    }

    static bool IsSupportedArgument(const RawArg& arg)
    {
        if(ck::is_navi3_supported())
        {
            if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
            {
                printf("DeviceOp: Acc0 Type err");
                return false;
            }

            if constexpr(!(is_same_v<Acc1DataType, float> || is_same_v<Acc1DataType, int32_t>))
            {
                printf("DeviceOp: Acc1 Type err");
                return false;
            }
        }
        else
        {
            printf("DeviceOp: Arch err");
            return false;
        }

        constexpr index_t array_size = 4;
        ck::index_t G0               = arg.G0_;
        ck::index_t G1               = arg.G1_;
        ck::index_t M                = arg.M_;
        ck::index_t N                = arg.N_;
        ck::index_t K                = arg.K_;
        ck::index_t O                = arg.O_;
        bool input_permute           = arg.input_permute_;
        bool output_permute          = arg.output_permute_;

        std::array<ck::index_t, array_size> a_gs_ms_ks_lengths{G0, G1, M, K};
        std::array<ck::index_t, array_size> a_gs_ms_ks_strides =
            input_permute ? std::array<ck::index_t, array_size>{M * G1 * K, K, G1 * K, 1}
                          // A layout [G0, M, G1, K]
                          : std::array<ck::index_t, array_size>{
                                G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]

        std::array<ck::index_t, array_size> b0_gs_ns_ks_lengths{G0, G1, N, K};
        std::array<ck::index_t, array_size> b0_gs_ns_ks_strides =
            input_permute ? std::array<ck::index_t, array_size>{N * G1 * K, K, G1 * K, 1}
                          // B0 layout [G0, N, G1, K]
                          : std::array<ck::index_t, array_size>{
                                G1 * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]

        std::array<ck::index_t, array_size> b1_gs_os_ns_lengths{G0, G1, O, N};
        std::array<ck::index_t, array_size> b1_gs_os_ns_strides =
            input_permute ? std::array<ck::index_t, array_size>{N * G1 * O, O, 1, G1 * O}
                          // B1 layout [G0, N, G1, O]
                          : std::array<ck::index_t, array_size>{
                                G1 * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]

        std::array<ck::index_t, array_size> c_gs_ms_os_lengths{G0, G1, M, O};
        std::array<ck::index_t, array_size> c_gs_ms_os_strides =
            output_permute ? std::array<ck::index_t, array_size>{M * G1 * O, O, G1 * O, 1}
                           // C layout [G0, M, G1, O]
                           : std::array<ck::index_t, array_size>{
                                 G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]

        const auto a_grid_desc =
            DeviceOp::MakeAGridDescriptor(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
        const auto b0_grid_desc =
            DeviceOp::MakeB0GridDescriptor(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
        const auto b1_grid_desc =
            DeviceOp::MakeB1GridDescriptor(b1_gs_os_ns_lengths, b1_gs_os_ns_strides);
        const auto c_grid_desc_m_n =
            DeviceOp::Transform::MakeCGridDescriptor_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);

        const auto block_2_ctile_map = GridwiseOp::MakeDefaultBlock2CTileMap(c_grid_desc_m_n, 1, 1);

        const auto c_grid_desc_g_m_n =
            DeviceOp::Transform::MakeCGridDescriptor_G_M_N(c_gs_ms_os_lengths, c_gs_ms_os_strides);
        index_t batch_count = c_grid_desc_g_m_n.GetLength(Number<0>{});

        if(!GridwiseOp::CheckValidity(
               a_grid_desc, b0_grid_desc, b1_grid_desc, c_grid_desc_m_n, block_2_ctile_map))
        {
            return false;
        }

        // Check if C permute dimension matches GEMM + GEMM shape
        const index_t c_g = c_grid_desc_g_m_n.GetLength(I0); // unpadded

        if(!(c_g == batch_count))
        {
            printf("DeviceOp: BatchCount err");
            return false;
        }

        // Note: we need raw lengths since threadwise copy can not handle vector load when part of
        // vector is out of bounds
        // Note: need lowest dim in Ms/Ns/Ks/Os, not merged M/N/K/O
        const auto MzRaw = M;
        const auto LzRaw = N;
        const auto KzRaw = K;
        const auto NzRaw = O;

        // Check scalar per vector requirement
        const auto a_extent_lowest  = ABlockTransferSrcVectorDim == 2 ? KzRaw : MzRaw;
        const auto b0_extent_lowest = B0BlockTransferSrcVectorDim == 2 ? KzRaw : LzRaw;
        const auto b1_extent_lowest = B1BlockTransferSrcVectorDim == 2 ? LzRaw : NzRaw;
        const auto c_extent_lowest  = NzRaw;

        if(!(a_extent_lowest % ABlockTransferSrcScalarPerVector == 0 &&
             b0_extent_lowest % B0BlockTransferSrcScalarPerVector == 0 &&
             b1_extent_lowest % B1BlockTransferSrcScalarPerVector == 0 &&
             c_extent_lowest % CShuffleBlockTransferScalarPerVector_NPerBlock == 0))
        {
            printf("DeviceOp: Data Transfer Vector scalar err");
            return false;
        }

        std::array<index_t, NumDimG + NumDimM + NumDimN> a_mz_kz_strides_{
            a_gs_ms_ks_strides[NumDimG + NumDimM - 1],
            a_gs_ms_ks_strides[NumDimG + NumDimM + NumDimK - 1]};
        std::array<index_t, NumDimG + NumDimM + NumDimN> b0_lz_kz_strides_{
            b0_gs_ns_ks_strides[NumDimG + NumDimL - 1],
            b0_gs_ns_ks_strides[NumDimG + NumDimL + NumDimK - 1]};
        std::array<index_t, NumDimG + NumDimM + NumDimN> b1_nz_lz_strides_{
            b1_gs_os_ns_strides[NumDimG + NumDimN - 1],
            b1_gs_os_ns_strides[NumDimG + NumDimN + NumDimL - 1]};
        std::array<index_t, NumDimG + NumDimM + NumDimN> c_mz_nz_strides_{
            c_gs_ms_os_strides[NumDimG + NumDimM - 1],
            c_gs_ms_os_strides[NumDimG + NumDimM + NumDimN - 1]};

        // Check vector load/store requirement
        const auto a_stride_lowest =
            ABlockTransferSrcVectorDim == 2 ? a_mz_kz_strides_[1] : a_mz_kz_strides_[0];
        const auto b0_stride_lowest =
            B0BlockTransferSrcVectorDim == 2 ? b0_lz_kz_strides_[1] : b0_lz_kz_strides_[0];
        const auto b1_stride_lowest =
            B1BlockTransferSrcVectorDim == 2 ? b1_nz_lz_strides_[1] : b1_nz_lz_strides_[0];
        const auto c_stride_lowest = c_mz_nz_strides_[1];

        if(!(a_stride_lowest == 1 || b0_stride_lowest == 1 || b1_stride_lowest == 1 ||
             c_stride_lowest == 1))
        {
            printf("DeviceOp: Data Vectorize transfer err");
            return false;
        }

        return true;
    }

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

    struct SelfAttnArg : public BaseArgument
    {
        SelfAttnArg(const ADataType* p_qkv_grid,
                    CDataType* p_out_grid,
                    index_t batch_size,
                    index_t sequence_length,
                    index_t head_count,
                    index_t head_size,
                    float alpha)
            : p_qkv_grid_{p_qkv_grid},
              p_out_grid_{p_out_grid},
              batch_size_{batch_size},
              sequence_length_{sequence_length},
              head_count_{head_count},
              head_size_{head_size},
              alpha_{alpha}
        {
        }
        // Pointers
        const ADataType* p_qkv_grid_;
        CDataType* p_out_grid_;

        // Raw Problem Size
        index_t batch_size_;
        index_t sequence_length_;
        index_t head_count_;
        index_t head_size_;
        float alpha_;
    };

    static auto MakeSelfAttnArgument(const ADataType* p_qkv_grid,
                                     CDataType* p_out_grid,
                                     index_t batch_size,
                                     index_t sequence_length,
                                     index_t head_count,
                                     index_t head_size,
                                     float alpha)
    {
        return SelfAttnArg{
            p_qkv_grid, p_out_grid, batch_size, sequence_length, head_count, head_size, alpha};
    }

    struct CrossAttnArg : public BaseArgument
    {
        CrossAttnArg(const ADataType* p_q_grid,
                     const B0DataType* p_kv_grid,
                     CDataType* p_out_grid,
                     index_t batch_size,
                     index_t q_sequence_length,
                     index_t kv_sequence_length,
                     index_t head_count,
                     index_t head_size,
                     float alpha)
            : p_q_grid_{p_q_grid},
              p_kv_grid_{p_kv_grid},
              p_out_grid_{p_out_grid},
              batch_size_{batch_size},
              q_sequence_length_{q_sequence_length},
              kv_sequence_length_{kv_sequence_length},
              head_count_{head_count},
              head_size_{head_size},
              alpha_{alpha}
        {
        }
        // Pointers
        const ADataType* p_q_grid_;
        const B0DataType* p_kv_grid_;
        CDataType* p_out_grid_;

        // Raw Problem Size
        index_t batch_size_;
        index_t q_sequence_length_;
        index_t kv_sequence_length_;
        index_t head_count_;
        index_t head_size_;
        float alpha_;
    };

    static auto MakeCrossAttnArgument(const ADataType* p_q_grid,
                                      const B0DataType* p_kv_grid,
                                      CDataType* p_out_grid,
                                      index_t batch_size,
                                      index_t q_sequence_length,
                                      index_t kv_sequence_length,
                                      index_t head_count,
                                      index_t head_size,
                                      float alpha)
    {
        return CrossAttnArg{p_q_grid,
                            p_kv_grid,
                            p_out_grid,
                            batch_size,
                            q_sequence_length,
                            kv_sequence_length,
                            head_count,
                            head_size,
                            alpha};
    }

    // Argument
    struct Argument : public BaseArgument
    {
        Argument(
            const ADataType* p_a_grid,
            const B0DataType* p_b0_grid,
            const B1DataType* p_b1_grid,
            CDataType* p_c_grid,
            const std::array<void*, NumAcc0Bias> p_acc0_biases,
            const std::array<void*, NumAcc1Bias> p_acc1_biases,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ls_ks_lengths,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ls_ks_strides,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_ns_ls_lengths,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_ns_ls_strides,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_ns_lengths,
            const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_ns_strides,
            const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ls_lengths,
            const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ls_strides,
            const std::array<std::vector<ck::index_t>, NumAcc1Bias> acc1_biases_gs_ms_ns_lengths,
            const std::array<std::vector<ck::index_t>, NumAcc1Bias> acc1_biases_gs_ms_ns_strides,
            const index_t M01,
            const index_t N01,
            AElementwiseOperation a_element_op,
            B0ElementwiseOperation b0_element_op,
            AccElementwiseOperation acc_element_op,
            B1ElementwiseOperation b1_element_op,
            CElementwiseOperation c_element_op)
            : p_a_grid_{p_a_grid},
              p_b0_grid_{p_b0_grid},
              p_b1_grid_{p_b1_grid},
              p_c_grid_{p_c_grid},
              a_grid_desc{DeviceOp::MakeAGridDescriptor(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
              b0_grid_desc{
                  DeviceOp::MakeB0GridDescriptor(b0_gs_ls_ks_lengths, b0_gs_ls_ks_strides)},
              b1_grid_desc{
                  DeviceOp::MakeB1GridDescriptor(b1_gs_ns_ls_lengths, b1_gs_ns_ls_strides)},
              c_grid_desc_m_n_{
                  Transform::MakeCGridDescriptor_M_N(c_gs_ms_ns_lengths, c_gs_ms_ns_strides)},
              a_grid_desc_g_m_k_{
                  Transform::MakeAGridDescriptor_G_M_K(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
              b0_grid_desc_g_l_k_{
                  Transform::MakeB0GridDescriptor_G_N_K(b0_gs_ls_ks_lengths, b0_gs_ls_ks_strides)},
              b1_grid_desc_g_n_l_{
                  Transform::MakeB1GridDescriptor_G_N_K(b1_gs_ns_ls_lengths, b1_gs_ns_ls_strides)},
              c_grid_desc_g_m_n_{
                  Transform::MakeCGridDescriptor_G_M_N(c_gs_ms_ns_lengths, c_gs_ms_ns_strides)},
              c_grid_desc_mblock_mperblock_nblock_nperblock_{},
              block_2_ctile_map_{GridwiseOp::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01)},
              a_element_op_{a_element_op},
              b0_element_op_{b0_element_op},
              acc_element_op_{acc_element_op},
              b1_element_op_{b1_element_op},
              c_element_op_{c_element_op},
              c0_matrix_mask_{b0_grid_desc_g_l_k_.GetLength(I1)},
              raw_lengths_mz_lz_kz_nz_{a_gs_ms_ks_lengths[NumDimG + NumDimM - 1],
                                       b0_gs_ls_ks_lengths[NumDimG + NumDimL - 1],
                                       b0_gs_ls_ks_lengths[NumDimG + NumDimL + NumDimK - 1],
                                       b1_gs_ns_ls_lengths[NumDimG + NumDimN - 1]},
              a_mz_kz_strides_{a_gs_ms_ks_strides[NumDimG + NumDimM - 1],
                               a_gs_ms_ks_strides[NumDimG + NumDimM + NumDimK - 1]},
              b0_lz_kz_strides_{b0_gs_ls_ks_strides[NumDimG + NumDimL - 1],
                                b0_gs_ls_ks_strides[NumDimG + NumDimL + NumDimK - 1]},
              b1_nz_lz_strides_{b1_gs_ns_ls_strides[NumDimG + NumDimN - 1],
                                b1_gs_ns_ls_strides[NumDimG + NumDimN + NumDimL - 1]},
              c_mz_nz_strides_{c_gs_ms_ns_strides[NumDimG + NumDimM - 1],
                               c_gs_ms_ns_strides[NumDimG + NumDimM + NumDimN - 1]},
              batch_count_{c_grid_desc_g_m_n_.GetLength(I0)},
              compute_ptr_offset_of_batch_{
                  a_grid_desc_g_m_k_, b0_grid_desc_g_l_k_, b1_grid_desc_g_n_l_, c_grid_desc_g_m_n_}
        {
            // TODO ANT: implement bias addition
            ignore = p_acc0_biases;
            ignore = p_acc1_biases;
            ignore = acc0_biases_gs_ms_ls_lengths;
            ignore = acc0_biases_gs_ms_ls_strides;
            ignore = acc1_biases_gs_ms_ns_lengths;
            ignore = acc1_biases_gs_ms_ns_strides;

            if(GridwiseOp::CheckValidity(
                   a_grid_desc, b0_grid_desc, b1_grid_desc, c_grid_desc_m_n_, block_2_ctile_map_))
            {
                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
                    GridwiseOp::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
                        c_grid_desc_m_n_);
            }
        }

        // Pointers
        const ADataType* p_a_grid_;
        const B0DataType* p_b0_grid_;
        const B1DataType* p_b1_grid_;
        CDataType* p_c_grid_;

        // Tensor Descriptors
        AGridDesc a_grid_desc;
        B0GridDesc b0_grid_desc;
        B1GridDesc b1_grid_desc;
        CGridDesc_M_N c_grid_desc_m_n_;

        AGridDesc_G_M_K a_grid_desc_g_m_k_;
        B0GridDesc_G_L_K b0_grid_desc_g_l_k_;
        B1GridDesc_G_N_L b1_grid_desc_g_n_l_;
        CGridDesc_G_M_N c_grid_desc_g_m_n_;

        typename GridwiseOp::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
            c_grid_desc_mblock_mperblock_nblock_nperblock_;

        // Block to Tile mapping
        typename GridwiseOp::DefaultBlock2CTileMap block_2_ctile_map_;

        // ElementwiseOp
        AElementwiseOperation a_element_op_;
        B0ElementwiseOperation b0_element_op_;
        AccElementwiseOperation acc_element_op_;
        B1ElementwiseOperation b1_element_op_;
        CElementwiseOperation c_element_op_;

        // check C0 masking and padding
        C0MatrixMask c0_matrix_mask_;

        // Strides for the last M/N/K dimensions of A/B0/B1/C
        //   for sanity check of vector load/store
        std::array<index_t, NumDimG + NumDimM + NumDimN> raw_lengths_mz_lz_kz_nz_;
        std::array<index_t, NumDimG + NumDimM + NumDimN> a_mz_kz_strides_;
        std::array<index_t, NumDimG + NumDimM + NumDimN> b0_lz_kz_strides_;
        std::array<index_t, NumDimG + NumDimM + NumDimN> b1_nz_lz_strides_;
        std::array<index_t, NumDimG + NumDimM + NumDimN> c_mz_nz_strides_;

        index_t batch_count_;
        // Batch Offset
        ComputeBasePtrOfStridedBatch compute_ptr_offset_of_batch_;
    };

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

        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
            const auto M0 = math::integer_divide_ceil(arg.sequence_length_, MPerBlock);
            const auto N0 = math::integer_divide_ceil(arg.head_size_, NPerBlock);

            const index_t grid_size = arg.batch_size_ * arg.head_count_ * M0 * N0;
            const auto K            = arg.head_size_;

            auto launch_kernel = [&](auto has_main_k_block_loop) {
                const auto kernel = kernel_wmma_self_attention_forward<DeviceOp,
                                                                       GridwiseOp,
                                                                       ADataType,
                                                                       CDataType,
                                                                       AElementwiseOperation,
                                                                       B0ElementwiseOperation,
                                                                       AccElementwiseOperation,
                                                                       B1ElementwiseOperation,
                                                                       CElementwiseOperation,
                                                                       has_main_k_block_loop>;

                return launch_and_time_kernel(stream_config,
                                              kernel,
                                              dim3(grid_size),
                                              dim3(BlockSize),
                                              0,
                                              arg.p_qkv_grid_,
                                              arg.p_out_grid_,
                                              arg.batch_size_,
                                              arg.sequence_length_,
                                              arg.head_count_,
                                              arg.head_size_,
                                              arg.alpha_);
            };

            if(GridwiseOp::CalculateHasMainKBlockLoop(K))
            {
                return launch_kernel(integral_constant<bool, true>{});
            }
            else
            {
                return launch_kernel(integral_constant<bool, false>{});
            }
        }

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

    static auto MakeSelfAttnInvoker() { return SelfAttnInvoker{}; }

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

        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
            const auto M0 = math::integer_divide_ceil(arg.q_sequence_length_, MPerBlock);
            const auto N0 = math::integer_divide_ceil(arg.head_size_, NPerBlock);

            const index_t grid_size = arg.batch_size_ * arg.head_count_ * M0 * N0;
            const auto K            = arg.head_size_;

            auto launch_kernel = [&](auto has_main_k_block_loop) {
                const auto kernel = kernel_wmma_cross_attention_forward<DeviceOp,
                                                                        GridwiseOp,
                                                                        ADataType,
                                                                        B0DataType,
                                                                        CDataType,
                                                                        AElementwiseOperation,
                                                                        B0ElementwiseOperation,
                                                                        AccElementwiseOperation,
                                                                        B1ElementwiseOperation,
                                                                        CElementwiseOperation,
                                                                        has_main_k_block_loop>;

                return launch_and_time_kernel(stream_config,
                                              kernel,
                                              dim3(grid_size),
                                              dim3(BlockSize),
                                              0,
                                              arg.p_q_grid_,
                                              arg.p_kv_grid_,
                                              arg.p_out_grid_,
                                              arg.batch_size_,
                                              arg.q_sequence_length_,
                                              arg.kv_sequence_length_,
                                              arg.head_count_,
                                              arg.head_size_,
                                              arg.alpha_);
            };

            if(GridwiseOp::CalculateHasMainKBlockLoop(K))
            {
                return launch_kernel(integral_constant<bool, true>{});
            }
            else
            {
                return launch_kernel(integral_constant<bool, false>{});
            }
        }

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

    static auto MakeCrossAttnInvoker() { return CrossAttnInvoker{}; }

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

        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
        {
            const auto M0 = math::integer_divide_ceil(arg.M_, MPerBlock);
            const auto N0 = math::integer_divide_ceil(arg.O_, NPerBlock);

            const index_t grid_size = arg.G0_ * arg.G1_ * M0 * N0;
            const auto K            = arg.K_;
            // printf("HasKBlockLoop: %d\n", GridwiseOp::CalculateHasMainKBlockLoop(K));
            auto launch_kernel = [&](auto has_main_k_block_loop) {
                const auto kernel =
                    kernel_batched_gemm_softmax_gemm_wmma_cshuffle<DeviceOp,
                                                                   GridwiseOp,
                                                                   ADataType,
                                                                   B0DataType,
                                                                   B1DataType,
                                                                   CDataType,
                                                                   AElementwiseOperation,
                                                                   B0ElementwiseOperation,
                                                                   AccElementwiseOperation,
                                                                   B1ElementwiseOperation,
                                                                   CElementwiseOperation,
                                                                   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_b0_grid_,
                                              arg.p_b1_grid_,
                                              arg.p_c_grid_,
                                              arg.M_,
                                              arg.N_,
                                              arg.K_,
                                              arg.O_,
                                              arg.G0_,
                                              arg.G1_,
                                              arg.alpha_,
                                              arg.input_permute_,
                                              arg.output_permute_);
            };

            if(GridwiseOp::CalculateHasMainKBlockLoop(K))
            {
                return launch_kernel(integral_constant<bool, true>{});
            }
            else
            {
                return launch_kernel(integral_constant<bool, false>{});
            }
        }

        // 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;
    }
#if 0
    static bool IsSupportedArgument(const Argument& arg)
    {
        if(ck::is_navi3_supported())
        {
            if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
            {
                printf("DeviceOp: Acc0 Type err");
                return false;
            }

            if constexpr(!(is_same_v<Acc1DataType, float> || is_same_v<Acc1DataType, int32_t>))
            {
                printf("DeviceOp: Acc1 Type err");
                return false;
            }
        }
        else
        {
            printf("DeviceOp: Arch err");
            return false;
        }

        if(!GridwiseOp::CheckValidity(arg.a_grid_desc,
                                      arg.b0_grid_desc,
                                      arg.b1_grid_desc,
                                      arg.c_grid_desc_m_n_,
                                      arg.block_2_ctile_map_))
        {
            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); // unpadded

        if(!(c_g == arg.batch_count_))
        {
            printf("DeviceOp: BatchCount err");
            return false;
        }

        // Note: we need raw lengths since threadwise copy can not handle vector load when part of
        // vector is out of bounds
        // Note: need lowest dim in Ms/Ns/Ks/Os, not merged M/N/K/O
        const auto MzRaw = arg.raw_lengths_mz_lz_kz_nz_[0];
        const auto LzRaw = arg.raw_lengths_mz_lz_kz_nz_[1];
        const auto KzRaw = arg.raw_lengths_mz_lz_kz_nz_[2];
        const auto NzRaw = arg.raw_lengths_mz_lz_kz_nz_[3];

        // Check scalar per vector requirement
        const auto a_extent_lowest  = ABlockTransferSrcVectorDim == 2 ? KzRaw : MzRaw;
        const auto b0_extent_lowest = B0BlockTransferSrcVectorDim == 2 ? KzRaw : LzRaw;
        const auto b1_extent_lowest = B1BlockTransferSrcVectorDim == 2 ? LzRaw : NzRaw;
        const auto c_extent_lowest  = NzRaw;

        if(!(a_extent_lowest % ABlockTransferSrcScalarPerVector == 0 &&
             b0_extent_lowest % B0BlockTransferSrcScalarPerVector == 0 &&
             b1_extent_lowest % B1BlockTransferSrcScalarPerVector == 0 &&
             c_extent_lowest % CShuffleBlockTransferScalarPerVector_NPerBlock == 0))
        {
            printf("DeviceOp: Data Transfer Vector scalar err");
            return false;
        }

        // Check vector load/store requirement
        const auto a_stride_lowest =
            ABlockTransferSrcVectorDim == 2 ? arg.a_mz_kz_strides_[1] : arg.a_mz_kz_strides_[0];
        const auto b0_stride_lowest =
            B0BlockTransferSrcVectorDim == 2 ? arg.b0_lz_kz_strides_[1] : arg.b0_lz_kz_strides_[0];
        const auto b1_stride_lowest =
            B1BlockTransferSrcVectorDim == 2 ? arg.b1_nz_lz_strides_[1] : arg.b1_nz_lz_strides_[0];
        const auto c_stride_lowest = arg.c_mz_nz_strides_[1];

        if(!(a_stride_lowest == 1 || b0_stride_lowest == 1 || b1_stride_lowest == 1 ||
             c_stride_lowest == 1))
        {
            printf("DeviceOp: Data Vectorize transfer err");
            return false;
        }

        return true;
    }

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

    static auto MakeArgument(
        const ADataType* p_a,
        const B0DataType* p_b0,
        const B1DataType* p_b1,
        CDataType* p_c,
        const std::array<void*, NumAcc0Bias> p_acc0_biases,
        const std::array<void*, NumAcc1Bias> p_acc1_biases,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ls_ks_lengths,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ls_ks_strides,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_ns_ls_lengths,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_ns_ls_strides,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_ns_lengths,
        const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_ns_strides,
        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ls_lengths,
        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ls_strides,
        const std::array<std::vector<ck::index_t>, NumAcc1Bias> acc1_biases_gs_ms_ns_lengths,
        const std::array<std::vector<ck::index_t>, NumAcc1Bias> acc1_biases_gs_ms_ns_strides,
        AElementwiseOperation a_element_op,
        B0ElementwiseOperation b0_element_op,
        AccElementwiseOperation acc_element_op,
        B1ElementwiseOperation b1_element_op,
        CElementwiseOperation c_element_op)
    {
        return Argument{p_a,
                        p_b0,
                        p_b1,
                        p_c,
                        p_acc0_biases,
                        p_acc1_biases,
                        a_gs_ms_ks_lengths,
                        a_gs_ms_ks_strides,
                        b0_gs_ls_ks_lengths,
                        b0_gs_ls_ks_strides,
                        b1_gs_ns_ls_lengths,
                        b1_gs_ns_ls_strides,
                        c_gs_ms_ns_lengths,
                        c_gs_ms_ns_strides,
                        acc0_biases_gs_ms_ls_lengths,
                        acc0_biases_gs_ms_ls_strides,
                        acc1_biases_gs_ms_ns_lengths,
                        acc1_biases_gs_ms_ns_strides,
                        1,
                        1,
                        a_element_op,
                        b0_element_op,
                        acc_element_op,
                        b1_element_op,
                        c_element_op};
    }
#endif

    // polymorphic
    std::unique_ptr<BaseArgument> MakeArgumentPointer(
        const void* p_a,
        const void* p_b0,
        const void* p_b1,
        void* p_c,
        const std::array<void*, NumAcc0Bias> p_acc0_biases,
        const std::array<void*, NumAcc1Bias> p_acc1_biases,
        const std::vector<index_t>& a_gs_ms_ks_lengths,
        const std::vector<index_t>& a_gs_ms_ks_strides,
        const std::vector<index_t>& b0_gs_ls_ks_lengths,
        const std::vector<index_t>& b0_gs_ls_ks_strides,
        const std::vector<index_t>& b1_gs_ns_ls_lengths,
        const std::vector<index_t>& b1_gs_ns_ls_strides,
        const std::vector<index_t>& c_gs_ms_ns_lengths,
        const std::vector<index_t>& c_gs_ms_ns_strides,
        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ls_lengths,
        const std::array<std::vector<ck::index_t>, NumAcc0Bias> acc0_biases_gs_ms_ls_strides,
        const std::array<std::vector<ck::index_t>, NumAcc1Bias> acc1_biases_gs_ms_ns_lengths,
        const std::array<std::vector<ck::index_t>, NumAcc1Bias> acc1_biases_gs_ms_ns_strides,
        AElementwiseOperation a_element_op,
        B0ElementwiseOperation b0_element_op,
        AccElementwiseOperation acc_element_op,
        B1ElementwiseOperation b1_element_op,
        CElementwiseOperation c_element_op) override
    {
        std::array<index_t, NumDimG + NumDimM + NumDimN> a_lengths;
        std::array<index_t, NumDimG + NumDimM + NumDimN> a_strides;
        std::array<index_t, NumDimG + NumDimM + NumDimN> b0_lengths;
        std::array<index_t, NumDimG + NumDimM + NumDimN> b0_strides;
        std::array<index_t, NumDimG + NumDimM + NumDimN> b1_lengths;
        std::array<index_t, NumDimG + NumDimM + NumDimN> b1_strides;
        std::array<index_t, NumDimG + NumDimM + NumDimN> c_lengths;
        std::array<index_t, NumDimG + NumDimM + NumDimN> c_strides;
        std::transform(a_gs_ms_ks_lengths.begin(),
                       a_gs_ms_ks_lengths.end(),
                       a_lengths.begin(),
                       [](index_t i) { return i; });
        std::transform(a_gs_ms_ks_strides.begin(),
                       a_gs_ms_ks_strides.end(),
                       a_strides.begin(),
                       [](index_t i) { return i; });
        std::transform(b0_gs_ls_ks_lengths.begin(),
                       b0_gs_ls_ks_lengths.end(),
                       b0_lengths.begin(),
                       [](index_t i) { return i; });
        std::transform(b0_gs_ls_ks_strides.begin(),
                       b0_gs_ls_ks_strides.end(),
                       b0_strides.begin(),
                       [](index_t i) { return i; });
        std::transform(b1_gs_ns_ls_lengths.begin(),
                       b1_gs_ns_ls_lengths.end(),
                       b1_lengths.begin(),
                       [](index_t i) { return i; });
        std::transform(b1_gs_ns_ls_strides.begin(),
                       b1_gs_ns_ls_strides.end(),
                       b1_strides.begin(),
                       [](index_t i) { return i; });
        std::transform(c_gs_ms_ns_lengths.begin(),
                       c_gs_ms_ns_lengths.end(),
                       c_lengths.begin(),
                       [](index_t i) { return i; });
        std::transform(c_gs_ms_ns_strides.begin(),
                       c_gs_ms_ns_strides.end(),
                       c_strides.begin(),
                       [](index_t i) { return i; });
        return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
                                          static_cast<const B0DataType*>(p_b0),
                                          static_cast<const B1DataType*>(p_b1),
                                          static_cast<CDataType*>(p_c),
                                          p_acc0_biases,
                                          p_acc1_biases,
                                          a_lengths,
                                          a_strides,
                                          b0_lengths,
                                          b0_strides,
                                          b1_lengths,
                                          b1_strides,
                                          c_lengths,
                                          c_strides,
                                          acc0_biases_gs_ms_ls_lengths,
                                          acc0_biases_gs_ms_ls_strides,
                                          acc1_biases_gs_ms_ns_lengths,
                                          acc1_biases_gs_ms_ns_strides,
                                          1,
                                          1,
                                          a_element_op,
                                          b0_element_op,
                                          acc_element_op,
                                          b1_element_op,
                                          c_element_op);
    }

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

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

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

        std::map<LoopScheduler, std::string> LoopSchedToString{
            {LoopScheduler::Default, "Default"}, {LoopScheduler::Interwave, "Interwave"}};

        std::map<PipelineVersion, std::string> PipelineVersionToString{{PipelineVersion::v1, "v1"},
                                                                       {PipelineVersion::v2, "v2"}};

        // clang-format off
        str << "DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle"
            << "<"
            << BlockSize << ", "
            << MPerBlock << ", "
            << LPerBlock << ", "
            << KPerBlock << ", "
            << AK1 << ", "
            << BK1 << ", "
            << MPerBlock << ", "
            << NPerBlock << ", "
            << LTilePerBlock << ", "
            << L1 << ", "
            << getGemmSpecializationString(GemmSpec) << ", "
            << "ASpec" << getTensorSpecializationString(ASpec) << ", "
            << "B0Spec" << getTensorSpecializationString(B0Spec) << ", "
            << "B1Spec" << getTensorSpecializationString(B1Spec) << ", "
            << "CSpec" << getTensorSpecializationString(CSpec) << ", "
            << getMaskingSpecializationString(MaskingSpec)
            << ">"
            << " AEnableLds: "
            << AEnableLds << ", "
            << "B0EnableLds: "
            << B0EnableLds << ", "
            << "B1EnableLds: "
            << B1EnableLds << ", "
            << "NumPrefetch: "
            << NumPrefetch << ", "
            << "LoopScheduler: "
            << LoopSchedToString[LoopSched] << ", "
            << "PipelineVersion: "
            << PipelineVersionToString[PipelineVer];
        // clang-format on

        return str.str();
    }
};

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