/*
 * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "cublasMMWrapper.h"
#include "cuda_utils.h"
#include "src/turbomind/macro.h"

#ifndef CUDART_VERSION
#error CUDART_VERSION Undefined!
#endif

namespace turbomind {
cublasMMWrapper::cublasMMWrapper(cublasHandle_t   cublas_handle,
                                 cublasLtHandle_t cublaslt_handle,
                                 cudaStream_t     stream,
                                 cublasAlgoMap*   cublas_algo_map,
                                 std::mutex*      mu,
                                 IAllocator*      allocator):
    cublas_handle_(cublas_handle),
    cublaslt_handle_(cublaslt_handle),
    stream_(stream),
    cublas_algo_map_(cublas_algo_map),
    mu_(mu),
    allocator_(allocator)
{
    //申请内存前读取环境变量确定weight_alyout格式
    //m_weightlayout_switch = 0 -->nn 形式的rocblas
    //m_weightlayout_switch = 1 -->tn pad 形式的rocblas
    //m_weightlayout_switch = 2 -->tn pad 形式的ck

    const char* env_weightlayout_str = std::getenv("LMDEPLOY_WEIGHTLAYOUT_SWITCH");
    if (env_weightlayout_str != nullptr) {
        m_weightlayout_switch = std::stoi(env_weightlayout_str);
    }

    TM_LOG_DEBUG(__PRETTY_FUNCTION__);
    if (allocator_ != nullptr) {
        cublas_workspace_ = allocator_->reMalloc(cublas_workspace_, CUBLAS_WORKSPACE_SIZE, false);

        //当采用rocblas的时候或者采用ck并开启dump功能的时候需要申请反量化模块
        if(m_weightlayout_switch ==1||m_weightlayout_switch==0)
        {
            //需要反量化后weight临时存储的空间
            printf("alloc space for deqeight\n");
            deweight_workspace_=allocator_->reMalloc(deweight_workspace_, DEQ_WORKSPACE_SIZE, false);  
            if(m_weightlayout_switch ==1)
            {
                printf("alloc space for xpading\n");
                printf("weight layout is tn pading rocblas\n");
                xpading_workspace_=allocator_->reMalloc(xpading_workspace_, XPAD_WORKSPACE_SIZE, false);
            }
        }
        else if(m_weightlayout_switch ==2)
        {
            printf("alloc space for ck workspace\n");
            printf("weight layout is tn ck\n"); 
            ck_workspace_ = allocator_->reMalloc(ck_workspace_, CK_WORKSPACE_SIZE, false);
        }
    }
	// hgemm-switch 0:fp32r,1:fp16r-fp32r,2:fp16r		----xzhou 20240427
    m_ihgemm_switch = 0;
    const char* env_var_value_str = std::getenv("LMDEPLOY_HGEMM_SWITCH");
    if (env_var_value_str != nullptr) {
        m_ihgemm_switch = std::stoi(env_var_value_str);
    }
    m_ihgemm_switch_n = 16;
    const char* env_n_value_str = std::getenv("LMDEPLOY_HGEMM_SWITCH_N");
    if (env_n_value_str != nullptr) {
        m_ihgemm_switch_n = std::stoi(env_n_value_str);
    }
    if(m_ihgemm_switch != 0) printf("hgemm_switch=%d, hgemm_switch_n_limit=%d\n", m_ihgemm_switch, m_ihgemm_switch_n);
}

#ifdef SPARSITY_ENABLED
cublasMMWrapper::cublasMMWrapper(cublasHandle_t     cublas_handle,
                                 cublasLtHandle_t   cublaslt_handle,
                                 cusparseLtHandle_t cusparselt_handle,
                                 cudaStream_t       stream,
                                 cublasAlgoMap*     cublas_algo_map,
                                 std::mutex*        mu,
                                 IAllocator*        allocator):
    cublas_handle_(cublas_handle),
    cublaslt_handle_(cublaslt_handle),
    cusparselt_handle_(cusparselt_handle),
    stream_(stream),
    cublas_algo_map_(cublas_algo_map),
    mu_(mu),
    allocator_(allocator)
{
    const char* env_weightlayout_str = std::getenv("LMDEPLOY_WEIGHTLAYOUT_SWITCH");
    if (env_weightlayout_str != nullptr) {
        m_weightlayout_switch = std::stoi(env_weightlayout_str);
    }
    
    TM_LOG_DEBUG(__PRETTY_FUNCTION__);
    if (allocator_ != nullptr) {
        cublas_workspace_ = allocator_->reMalloc(cublas_workspace_, CUBLAS_WORKSPACE_SIZE, false);

        //当采用rocblas的时候或者采用ck并开启dump功能的时候需要申请反量化模块
        if(m_weightlayout_switch ==1||m_weightlayout_switch==0)
        {
            //需要反量化后weight临时存储的空间
            printf("alloc space for deqeight\n");
            deweight_workspace_=allocator_->reMalloc(deweight_workspace_, DEQ_WORKSPACE_SIZE, false);  
            if(m_weightlayout_switch ==1)
            {
                printf("alloc space for xpading\n");
                printf("weight layout is tn pading rocblas\n");
                xpading_workspace_=allocator_->reMalloc(xpading_workspace_, XPAD_WORKSPACE_SIZE, false);
            }
        }
        else if(m_weightlayout_switch ==2)
        {
            printf("alloc space for ck workspace\n");
            printf("weight layout is tn ck\n"); 
            ck_workspace_ = allocator_->reMalloc(ck_workspace_, CK_WORKSPACE_SIZE, false);
        }
    }
}
#endif

cublasMMWrapper::~cublasMMWrapper()
{
    TM_LOG_DEBUG(__PRETTY_FUNCTION__);
    mu_ = nullptr;
    if (allocator_ != nullptr) {
        allocator_->free((void**)(&cublas_workspace_));
        if(m_weightlayout_switch ==1||m_weightlayout_switch==0)
        {
            //需要反量化后weight临时存储的空间
            printf("free space for deqeight\n");
            allocator_->free((void**)(&deweight_workspace_)); 
            if(m_weightlayout_switch ==1)
            {
                printf("free space for xpading\n");
                allocator_->free((void**)(&xpading_workspace_)); 
            }
        }
        else if(m_weightlayout_switch ==2)
        {
            printf("free space for ck workspace\n");
            allocator_->free((void**)(&ck_workspace_)); 
        }
        allocator_ = nullptr;
    }
}

cublasMMWrapper::cublasMMWrapper(const cublasMMWrapper& wrapper):
    cublas_handle_(wrapper.cublas_handle_),
    cublaslt_handle_(wrapper.cublaslt_handle_),
#ifdef SPARSITY_ENABLED
    cusparselt_handle_(wrapper.cusparselt_handle_),
#endif
    stream_(wrapper.stream_),
    cublas_algo_map_(wrapper.cublas_algo_map_),
    mu_(wrapper.mu_),
    allocator_(wrapper.allocator_)
{
    const char* env_weightlayout_str = std::getenv("LMDEPLOY_WEIGHTLAYOUT_SWITCH");
    if (env_weightlayout_str != nullptr) {
        m_weightlayout_switch = std::stoi(env_weightlayout_str);
    }  
    
    TM_LOG_DEBUG(__PRETTY_FUNCTION__);
    if (allocator_ != nullptr) {
        cublas_workspace_ = allocator_->reMalloc(cublas_workspace_, CUBLAS_WORKSPACE_SIZE, false);
        
        //当采用rocblas的时候或者采用ck并开启dump功能的时候需要申请反量化模块
        if(m_weightlayout_switch ==1||m_weightlayout_switch==0)
        {
            //需要反量化后weight临时存储的空间
            //("alloc space for deqeight\n");
            deweight_workspace_=allocator_->reMalloc(deweight_workspace_, DEQ_WORKSPACE_SIZE, false);  
            if(m_weightlayout_switch ==1)
            {
                printf("alloc space for xpading\n");
                printf("weight layout is tn pading rocblas\n");
                xpading_workspace_=allocator_->reMalloc(xpading_workspace_, XPAD_WORKSPACE_SIZE, false);
            }
        }
        else if(m_weightlayout_switch ==2)
        {
            printf("alloc space for ck workspace\n");
            printf("weight layout is tn ck\n"); 
            ck_workspace_ = allocator_->reMalloc(ck_workspace_, CK_WORKSPACE_SIZE, false);
        }
    }
}

void cublasMMWrapper::Gemm(cublasOperation_t transa,
                           cublasOperation_t transb,
                           const int         m,
                           const int         n,
                           const int         k,
                           const void*       alpha,
                           const void*       A,
                           cudaDataType_t    Atype,
                           int               lda,
                           const void*       B,
                           cudaDataType_t    Btype,
                           int               ldb,
                           const void*       beta,
                           void*             C,
                           cudaDataType_t    Ctype,
                           int               ldc,
                           cudaDataType_t    computeType,
                           cublasGemmAlgo_t  algo)
{
    TM_LOG_DEBUG(__PRETTY_FUNCTION__);
    mu_->lock();
	// hgemm-switch  ----xzhou 20240427
    if(m_ihgemm_switch == 1 && (m == 5120 || m == 4096 || m == 12288 || m == 11008) && n <= m_ihgemm_switch_n && Atype == CUDA_R_16F){
        computeType = CUDA_R_16F;
    }
    check_cuda_error(cublasGemmEx(cublas_handle_,
                                  transa,
                                  transb,
                                  m,
                                  n,
                                  k,
                                  alpha,
                                  A,
                                  Atype,
                                  lda,
                                  B,
                                  Btype,
                                  ldb,
                                  beta,
                                  C,
                                  Ctype,
                                  ldc,
                                  computeType,
                                  algo));
    sync_check_cuda_error();
    mu_->unlock();
}

void cublasMMWrapper::Gemm(cublasOperation_t transa,
                           cublasOperation_t transb,
                           const int         m,
                           const int         n,
                           const int         k,
                           const void*       A,
                           const int         lda,
                           const void*       B,
                           const int         ldb,
                           void*             C,
                           const int         ldc)
{
    TM_LOG_DEBUG(__PRETTY_FUNCTION__);
    Gemm(transa, transb, m, n, k, A, lda, B, ldb, C, ldc, 1.0f, 0.0f);
}

void cublasMMWrapper::Gemm(cublasOperation_t transa,
                           cublasOperation_t transb,
                           const int         m,
                           const int         n,
                           const int         k,
                           const void*       A,
                           const int         lda,
                           const void*       B,
                           const int         ldb,
                           void*             C,
                           const int         ldc,
                           float             f_alpha,
                           float             f_beta)
{
    TM_LOG_DEBUG(__PRETTY_FUNCTION__);
    half h_alpha = (half)(f_alpha);
    half h_beta  = (half)(f_beta);

    mu_->lock();
    // TODO: default cublas libs
    cudaDataType_t computeType = computeType_;
	// hgemm-switch ------xzhou 20240427
    if(m_ihgemm_switch == 1 && (m == 5120 || m == 4096 || m == 12288 || m == 11008) && n <= m_ihgemm_switch_n && Atype_ == CUDA_R_16F){
        computeType = CUDA_R_16F;
    }
    int  is_fp16_computeType = computeType == CUDA_R_16F ? 1 : 0;
    bool using_cublasLt      = (Atype_ == CUDA_R_16F) ? true : false;
    int  batch_count         = 1;
    // fp32 use cublas as default
    // fp16 use cublasLt as default
    const void* alpha = is_fp16_computeType ? reinterpret_cast<void*>(&h_alpha) : reinterpret_cast<void*>(&f_alpha);
    const void* beta  = is_fp16_computeType ? reinterpret_cast<void*>(&h_beta) : reinterpret_cast<void*>(&f_beta);

    int findAlgo = cublas_algo_map_->isExist(batch_count, m, n, k, getCublasDataType(Atype_));

    cublasLtMatmulAlgo_info info = cublas_algo_map_->getAlgo(batch_count, m, n, k, getCublasDataType(Atype_));
    if (findAlgo) {
        if (info.stages != -1) {
            using_cublasLt = false;
        }
        else {
            using_cublasLt = false;
        }
    }


    // if (using_cublasLt) {
//     if (0) {
//         cublasLtMatmulDesc_t   operationDesc = NULL;
//         cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
//         cudaDataType_t         scaleType;
// #if (CUDART_VERSION >= 11000)
//         cublasComputeType_t computeType;
// #else
//         cudaDataType_t computeType;
// #endif

//         if (is_fp16_computeType) {
// #if (CUDART_VERSION >= 11000)
//             computeType = CUBLAS_COMPUTE_16F;
// #else
//             computeType = CUDA_R_16F;
// #endif
//             scaleType = CUDA_R_16F;
//         }
//         else {
// #if (CUDART_VERSION >= 11000)
//             computeType = CUBLAS_COMPUTE_32F;
// #else
//             computeType = CUDA_R_32F;
// #endif
//             scaleType = CUDA_R_32F;
//         }

//         // --------------------------------------
//         // Create descriptors for the original matrices
//         cublasLtMatrixLayoutCreate(&Adesc, Atype_, transa == CUBLAS_OP_N ? m : k, transa == CUBLAS_OP_N ? k : m, lda);
//         cublasLtMatrixLayoutCreate(&Bdesc, Btype_, transb == CUBLAS_OP_N ? k : n, transb == CUBLAS_OP_N ? n : k, ldb);
//         cublasLtMatrixLayoutCreate(&Cdesc, Ctype_, m, n, ldc);
// #if (CUDART_VERSION >= 11000)
//         cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
// #else
//         cublasLtMatmulDescCreate(&operationDesc, computeType);
// #endif

//         cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(cublasOperation_t));
//         cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(cublasOperation_t));

//         cublasLtMatmulAlgo_t algo;
//         void*                workSpace     = cublas_workspace_;
//         int                  workspaceSize = cublas_workspace_ == NULL ? 0 : CUBLAS_WORKSPACE_SIZE;
//         if (findAlgo) {
//             if (info.workspaceSize > workspaceSize) {
//                 findAlgo = 0;
//             }
//             else {
//                 cublasLtMatmulAlgoInit(
//                     cublaslt_handle_, computeType, scaleType, Atype_, Btype_, Ctype_, Ctype_, info.algoId, &algo);
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &(info.customOption), sizeof(info.customOption));
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &(info.tile), sizeof(info.tile));
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &(info.splitK_val), sizeof(info.splitK_val));
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(info.swizzle), sizeof(info.swizzle));
//                 cublasLtMatmulAlgoConfigSetAttribute(&algo,
//                                                      CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
//                                                      &(info.reductionScheme),
//                                                      sizeof(info.reductionScheme));

// // #if (CUDART_VERSION >= 11000)
// //                 cublasLtMatmulAlgoConfigSetAttribute(
// //                     &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
// // #endif

// #if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_INNER_SHAPE_ID, &(info.inner_shapeId), sizeof(info.inner_shapeId));
//                 cublasLtMatmulAlgoConfigSetAttribute(&algo,
//                                                      CUBLASLT_ALGO_CONFIG_CLUSTER_SHAPE_ID,
//                                                      &(info.cluster_shapeId),
//                                                      sizeof(info.cluster_shapeId));
// #elif (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH < 3)
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_MMA_SHAPE_ID, &(info.mma_shapeId), sizeof(info.mma_shapeId));
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_CGA_SHAPE_ID, &(info.cga_shapeId), sizeof(info.cga_shapeId));
//                 cublasLtMatmulAlgoConfigSetAttribute(
//                     &algo, CUBLASLT_ALGO_CONFIG_SCHEDULING_MODE, &(info.sche_mode), sizeof(info.sche_mode));
// #endif
//             }
//         }

//         // cublasLtMatmul(cublaslt_handle_,
//         //                operationDesc,
//         //                alpha,
//         //                A,
//         //                Adesc,
//         //                B,
//         //                Bdesc,
//         //                beta,
//         //                C,
//         //                Cdesc,
//         //                C,
//         //                Cdesc,
//         //                (findAlgo == 1 ? (&algo) : NULL),
//         //                workSpace,
//         //                workspaceSize,
//         //                stream_);

//         cublasLtMatmulDescDestroy(operationDesc);
//         cublasLtMatrixLayoutDestroy(Adesc);
//         cublasLtMatrixLayoutDestroy(Bdesc);
//         cublasLtMatrixLayoutDestroy(Cdesc);
//         sync_check_cuda_error();
//     }
//     else {
        int cublasAlgo = info.algoId;
        check_cuda_error(cublasGemmEx(cublas_handle_,
                                      transa,
                                      transb,
                                      m,
                                      n,
                                      k,
                                      alpha,
                                      A,
                                      Atype_,
                                      lda,
                                      B,
                                      Btype_,
                                      ldb,
                                      beta,
                                      C,
                                      Ctype_,
                                      ldc,
                                      computeType,
                                      static_cast<cublasGemmAlgo_t>(cublasAlgo)));
        sync_check_cuda_error();
    // }
    mu_->unlock();
}

void cublasMMWrapper::setFP32GemmConfig()
{
    Atype_       = CUDA_R_32F;
    Btype_       = CUDA_R_32F;
    Ctype_       = CUDA_R_32F;
    computeType_ = CUDA_R_32F;
}

void cublasMMWrapper::setFP16GemmConfig()
{
    Atype_       = CUDA_R_16F;
    Btype_       = CUDA_R_16F;
    Ctype_       = CUDA_R_16F;
    computeType_ = CUDA_R_32F;
}

#ifdef ENABLE_BF16
void cublasMMWrapper::setBF16GemmConfig()
{
    Atype_       = CUDA_R_16BF;
    Btype_       = CUDA_R_16BF;
    Ctype_       = CUDA_R_16BF;
    computeType_ = CUDA_R_32F;
}
#endif

void cublasMMWrapper::setGemmConfig(cudaDataType_t aType,
                                    cudaDataType_t bType,
                                    cudaDataType_t cType,
                                    cudaDataType_t computeType)
{
    Atype_       = aType;
    Btype_       = bType;
    Ctype_       = cType;
    computeType_ = computeType;
}

CublasDataType cublasMMWrapper::getCublasDataType(cudaDataType_t data_type)
{
    if (data_type == CUDA_R_16F) {
        return HALF_DATATYPE;
    }
    else if (data_type == CUDA_R_32F) {
        return FLOAT_DATATYPE;
    }
#ifdef ENABLE_BF16
    else if (data_type == CUDA_R_16BF) {
        return BFLOAT16_DATATYPE;
    }
#endif
    return FLOAT_DATATYPE;
}

// #if (CUDART_VERSION >= 11000)
// // input, weight, output are row-major
// // only works for cublas 11.x
// void cublasMMWrapper::Gemm(cublasOperation_t transa,
//                            cublasOperation_t transb,
//                            const int         m,
//                            const int         n,
//                            const int         k,
//                            const void*       A,
//                            const int         lda,
//                            const void*       B,
//                            const int         ldb,
//                            const void*       bias,
//                            void*             C,
//                            const int         ldc)
// {
//     TM_LOG_DEBUG(__PRETTY_FUNCTION__);
//     cudaDataType_t      Atype, Btype, Ctype;
//     cublasComputeType_t computeType;
//     cudaDataType_t      scaleType;
//     float               alpha_float = 1.0f;
//     float               beta_float  = 0.0f;
//     half                alpha_half  = half(1.0f);
//     half                beta_half   = half(0.0f);
//     void *              alpha, *beta;

//     // int is_fp16_computeType = computeType_ == CUDA_R_16F ? 1 : 0;
//     if (Atype_ == CUDA_R_32F) {
//         computeType = CUBLAS_COMPUTE_32F_FAST_TF32;
//         Atype       = CUDA_R_32F;
//         Btype       = CUDA_R_32F;
//         Ctype       = CUDA_R_32F;
//         scaleType   = CUDA_R_32F;
//         alpha       = &alpha_float;
//         beta        = &beta_float;
//     }
//     else if (Atype_ == CUDA_R_16BF) {
//         computeType = CUBLAS_COMPUTE_32F_FAST_TF32;
//         Atype       = CUDA_R_16BF;
//         Btype       = CUDA_R_16BF;
//         Ctype       = CUDA_R_16BF;
//         scaleType   = CUDA_R_32F;
//         alpha       = &alpha_float;
//         beta        = &beta_float;
//     }
//     else {
//         computeType = CUBLAS_COMPUTE_16F;
//         Atype       = CUDA_R_16F;
//         Btype       = CUDA_R_16F;
//         Ctype       = CUDA_R_16F;
//         scaleType   = CUDA_R_16F;
//         alpha       = &alpha_half;
//         beta        = &beta_half;
//     }

//     cublasLtMatmulDesc_t   operationDesc = NULL;
//     cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
//     cublasLtEpilogue_t     epi = CUBLASLT_EPILOGUE_BIAS;
//     cublasLtMatrixLayoutCreate(&Adesc, Atype, (transa == CUBLAS_OP_N) ? m : k, (transa == CUBLAS_OP_N) ? k : m, lda);
//     cublasLtMatrixLayoutCreate(&Bdesc, Btype, (transb == CUBLAS_OP_N) ? k : n, (transb == CUBLAS_OP_N) ? n : k, ldb);
//     cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldc);

//     cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
//     cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(cublasOperation_t));
//     cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(cublasOperation_t));
//     cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epi, sizeof(cublasLtEpilogue_t));
//     cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(const void*));
//     // check_cuda_error(cublasLtMatmul(
//     //     cublaslt_handle_, operationDesc, alpha, A, Adesc, B, Bdesc, beta, C, Cdesc, C, Cdesc, NULL, NULL, 0, stream_));
//     cublasLtMatrixLayoutDestroy(Adesc);
//     cublasLtMatrixLayoutDestroy(Bdesc);
//     cublasLtMatrixLayoutDestroy(Cdesc);
//     cublasLtMatmulDescDestroy(operationDesc);
// }
// #endif
void cublasMMWrapper::setStream(cudaStream_t stream)
{
    stream_ = stream;
}

void cublasMMWrapper::stridedBatchedGemm(cublasOperation_t transa,
                                         cublasOperation_t transb,
                                         const int         m,
                                         const int         n,
                                         const int         k,
                                         const void*       A,
                                         const int         lda,
                                         const int64_t     strideA,
                                         const void*       B,
                                         const int         ldb,
                                         const int64_t     strideB,
                                         void*             C,
                                         const int         ldc,
                                         const int64_t     strideC,
                                         const int         batch_count,
                                         const float       f_alpha,
                                         const float       f_beta)
{
    half h_alpha = (half)f_alpha;
    half h_beta  = (half)f_beta;

    mu_->lock();
    int         is_fp16_computeType = computeType_ == CUDA_R_16F ? 1 : 0;
    const void* alpha =
        is_fp16_computeType ? reinterpret_cast<void*>(&h_alpha) : reinterpret_cast<const void*>(&f_alpha);
    const void* beta = is_fp16_computeType ? reinterpret_cast<void*>(&h_beta) : reinterpret_cast<const void*>(&f_beta);
    cublasLtMatmulAlgo_info info = cublas_algo_map_->getAlgo(batch_count, m, n, k, getCublasDataType(Atype_));

    check_cuda_error(cublasGemmStridedBatchedEx(cublas_handle_,
                                                transa,
                                                transb,
                                                m,
                                                n,
                                                k,
                                                alpha,
                                                A,
                                                Atype_,
                                                lda,
                                                strideA,
                                                B,
                                                Btype_,
                                                ldb,
                                                strideB,
                                                beta,
                                                C,
                                                Ctype_,
                                                ldc,
                                                strideC,
                                                batch_count,
                                                computeType_,
                                                static_cast<cublasGemmAlgo_t>(info.algoId)));

    mu_->unlock();
}

void cublasMMWrapper::stridedBatchedGemm(cublasOperation_t transa,
                                         cublasOperation_t transb,
                                         const int         m,
                                         const int         n,
                                         const int         k,
                                         const float       f_alpha,
                                         const void*       A,
                                         cudaDataType_t    AType,
                                         const int         lda,
                                         const int64_t     strideA,
                                         const void*       B,
                                         cudaDataType_t    BType,
                                         const int         ldb,
                                         const int64_t     strideB,
                                         const float       f_beta,
                                         void*             C,
                                         cudaDataType_t    CType,
                                         const int         ldc,
                                         const int64_t     strideC,
                                         const int         batch_count,
                                         cudaDataType_t    computeType)
{
    half h_alpha = (half)f_alpha;
    half h_beta  = (half)f_beta;

    mu_->lock();
    int         is_fp16_computeType = computeType == CUDA_R_16F ? 1 : 0;
    const void* alpha =
        is_fp16_computeType ? reinterpret_cast<void*>(&h_alpha) : reinterpret_cast<const void*>(&f_alpha);
    const void* beta = is_fp16_computeType ? reinterpret_cast<void*>(&h_beta) : reinterpret_cast<const void*>(&f_beta);
    cublasLtMatmulAlgo_info info = cublas_algo_map_->getAlgo(batch_count, m, n, k, getCublasDataType(Atype_));

    check_cuda_error(cublasGemmStridedBatchedEx(cublas_handle_,
                                                transa,
                                                transb,
                                                m,
                                                n,
                                                k,
                                                alpha,
                                                A,
                                                AType,
                                                lda,
                                                strideA,
                                                B,
                                                BType,
                                                ldb,
                                                strideB,
                                                beta,
                                                C,
                                                CType,
                                                ldc,
                                                strideC,
                                                batch_count,
                                                computeType,
                                                static_cast<cublasGemmAlgo_t>(info.algoId)));

    mu_->unlock();
}

void cublasMMWrapper::batchedGemm(cublasOperation_t  transa,
                                  cublasOperation_t  transb,
                                  const int          m,
                                  const int          n,
                                  const int          k,
                                  const void* const* A,
                                  const int          lda,
                                  const void* const* B,
                                  const int          ldb,
                                  void* const*       C,
                                  const int          ldc,
                                  const int          batch_count)
{
    float f_alpha = static_cast<float>(1.0f);
    float f_beta  = static_cast<float>(0.0f);

    half h_alpha = (half)1.0f;
    half h_beta  = (half)0.0f;

    mu_->lock();
    int         is_fp16_computeType = computeType_ == CUDA_R_16F ? 1 : 0;
    const void* alpha = is_fp16_computeType ? reinterpret_cast<void*>(&h_alpha) : reinterpret_cast<void*>(&f_alpha);
    const void* beta  = is_fp16_computeType ? reinterpret_cast<void*>(&h_beta) : reinterpret_cast<void*>(&f_beta);
    cublasLtMatmulAlgo_info info = cublas_algo_map_->getAlgo(batch_count, m, n, k, getCublasDataType(Atype_));

    check_cuda_error(cublasGemmBatchedEx(cublas_handle_,
                                         transa,
                                         transb,
                                         m,
                                         n,
                                         k,
                                         alpha,
                                         A,
                                         Atype_,
                                         lda,
                                         B,
                                         Btype_,
                                         ldb,
                                         beta,
                                         C,
                                         Ctype_,
                                         ldc,
                                         batch_count,
                                         computeType_,
                                         static_cast<cublasGemmAlgo_t>(info.algoId)));
    mu_->unlock();
}

bool cublasMMWrapper::isFuseBatchGemm(const int batch_count, const int m, const int k, const int n)
{
    CublasDataType data_type = getCublasDataType(Atype_);

    if (cublas_algo_map_->isExist(batch_count, m, k, n, data_type) == false
        || cublas_algo_map_->isExist(1, m, k, n, data_type) == false) {
        return false;
    }
    else {
        return cublas_algo_map_->getAlgo(batch_count, m, k, n, data_type).exec_time
               < 3 * cublas_algo_map_->getAlgo(1, m, k, n, data_type).exec_time;
    }
}

#ifdef SPARSITY_ENABLED
void cublasMMWrapper::SpGemm(cublasOperation_t transa,
                             cublasOperation_t transb,
                             const int         m,
                             const int         n,
                             const int         k,
                             const void*       A,
                             const void*       B,
                             void*             C)
{
    if (Atype_ != CUDA_R_16F || Btype_ != CUDA_R_16F || Ctype_ != CUDA_R_16F) {
        throw std::runtime_error("\n[TM][ERROR] sparse GEMM only supports FP16 data type now.");
    }
    static bool not_printed_fp32_accumulation_warning = true;
    if (computeType_ != CUDA_R_16F && not_printed_fp32_accumulation_warning) {
        printf("[TM][WARNING] cublasMMWrapper sets to FP32 compute type, "
               "but sparse gemm will use FP16 compute type since cusparselt "
               "supports FP16 accumulation only.\n");
        not_printed_fp32_accumulation_warning = false;
    }
    cusparseOrder_t     order = CUSPARSE_ORDER_COL;
    cusparseOperation_t opA = (transa == CUBLAS_OP_N) ? CUSPARSE_OPERATION_NON_TRANSPOSE : CUSPARSE_OPERATION_TRANSPOSE;
    cusparseOperation_t opB = (transb == CUBLAS_OP_N) ? CUSPARSE_OPERATION_NON_TRANSPOSE : CUSPARSE_OPERATION_TRANSPOSE;
    cusparseComputeType compute_type = CUSPARSE_COMPUTE_16F;
    cusparseLtMatmulDescriptor_t   matmul;
    cusparseLtMatmulAlgSelection_t alg_sel;
    cusparseLtMatmulPlan_t         plan;

    bool     is_rowmajor    = (order == CUSPARSE_ORDER_ROW);
    bool     isA_transposed = (opA != CUSPARSE_OPERATION_NON_TRANSPOSE);
    bool     isB_transposed = (opB != CUSPARSE_OPERATION_NON_TRANSPOSE);
    auto     num_A_rows     = (isA_transposed) ? k : m;
    auto     num_A_cols     = (isA_transposed) ? m : k;
    auto     num_B_rows     = (isB_transposed) ? n : k;
    auto     num_B_cols     = (isB_transposed) ? k : n;
    auto     num_C_rows     = m;
    auto     num_C_cols     = n;
    unsigned alignment      = 16;
    auto     lda            = (is_rowmajor) ? num_A_cols : num_A_rows;
    auto     ldb            = (is_rowmajor) ? num_B_cols : num_B_rows;
    auto     ldc            = (is_rowmajor) ? num_C_cols : num_C_rows;
    float    _alpha(1.0f);
    float    _beta(0.0f);

    char mark[256];
    sprintf(mark, "%d_%d_%d_%d", 1, m, n, k);
    if (sp_mat_A_desc_map_.find(mark) != sp_mat_A_desc_map_.end()) {
        CHECK_CUSPARSE(cusparseLtMatmulDescriptorInit(&cusparselt_handle_,
                                                      &matmul,
                                                      opA,
                                                      opB,
                                                      &sp_mat_A_desc_map_[mark],
                                                      &sp_mat_B_desc_map_[mark],
                                                      &sp_mat_C_desc_map_[mark],
                                                      &sp_mat_C_desc_map_[mark],
                                                      compute_type))
    }
    else {
        // initializing MatDesc takes a lot of time
        cusparseLtMatDescriptor_t mat_A, mat_B, mat_C;
        sp_mat_A_desc_map_[mark] = mat_A;
        sp_mat_B_desc_map_[mark] = mat_B;
        sp_mat_C_desc_map_[mark] = mat_C;
        CHECK_CUSPARSE(cusparseLtStructuredDescriptorInit(&cusparselt_handle_,
                                                          &sp_mat_A_desc_map_[mark],
                                                          num_A_rows,
                                                          num_A_cols,
                                                          lda,
                                                          alignment,
                                                          Atype_,
                                                          order,
                                                          CUSPARSELT_SPARSITY_50_PERCENT))
        CHECK_CUSPARSE(cusparseLtDenseDescriptorInit(
            &cusparselt_handle_, &sp_mat_B_desc_map_[mark], num_B_rows, num_B_cols, ldb, alignment, Btype_, order))
        CHECK_CUSPARSE(cusparseLtDenseDescriptorInit(
            &cusparselt_handle_, &sp_mat_C_desc_map_[mark], num_C_rows, num_C_cols, ldc, alignment, Ctype_, order))
        CHECK_CUSPARSE(cusparseLtMatmulDescriptorInit(&cusparselt_handle_,
                                                      &matmul,
                                                      opA,
                                                      opB,
                                                      &sp_mat_A_desc_map_[mark],
                                                      &sp_mat_B_desc_map_[mark],
                                                      &sp_mat_C_desc_map_[mark],
                                                      &sp_mat_C_desc_map_[mark],
                                                      compute_type))
    }
    mu_->lock();
    CHECK_CUSPARSE(
        cusparseLtMatmulAlgSelectionInit(&cusparselt_handle_, &alg_sel, &matmul, CUSPARSELT_MATMUL_ALG_DEFAULT))
    int alg = cublas_algo_map_->getSpAlgo(1, num_A_rows, num_B_cols, num_A_cols);
    CHECK_CUSPARSE(cusparseLtMatmulAlgSetAttribute(
        &cusparselt_handle_, &alg_sel, CUSPARSELT_MATMUL_ALG_CONFIG_ID, &alg, sizeof(alg)))
    size_t workspace_size;
    CHECK_CUSPARSE(cusparseLtMatmulGetWorkspace(&cusparselt_handle_, &alg_sel, &workspace_size))
    CHECK_CUSPARSE(cusparseLtMatmulPlanInit(&cusparselt_handle_, &plan, &matmul, &alg_sel, workspace_size))

    void*        d_workspace = nullptr;
    int          num_streams = 1;
    cudaStream_t streams[1]  = {stream_};
    CHECK_CUSPARSE(
        cusparseLtMatmul(&cusparselt_handle_, &plan, &_alpha, A, B, &_beta, C, C, d_workspace, streams, num_streams))
    CHECK_CUSPARSE(cusparseLtMatmulPlanDestroy(&plan))
    sync_check_cuda_error();
    mu_->unlock();
}

size_t cublasMMWrapper::getSparseMatrixSize(int m, int k)
{
    // Get a compressed matrix size of shape (m, k) used in cusparselt.
    auto            Atype_     = CUDA_R_16F;
    cusparseOrder_t order      = CUSPARSE_ORDER_COL;
    unsigned        alignment  = 16;
    int             num_A_rows = m;
    int             num_A_cols = k;
    int             lda        = num_A_rows;

    cusparseLtMatDescriptor_t mat_A;
    CHECK_CUSPARSE(cusparseLtStructuredDescriptorInit(&cusparselt_handle_,
                                                      &mat_A,
                                                      num_A_rows,
                                                      num_A_cols,
                                                      lda,
                                                      alignment,
                                                      Atype_,
                                                      order,
                                                      CUSPARSELT_SPARSITY_50_PERCENT));
    size_t compressed_size = 0;
    CHECK_CUSPARSE(cusparseLtSpMMACompressedSize2(&cusparselt_handle_, &mat_A, &compressed_size));
    return compressed_size;
}

void cublasMMWrapper::compressMatrix(const void* input, void* output, const int m, const int k)
{
    cusparseOrder_t           order = CUSPARSE_ORDER_COL;
    cusparseOperation_t       opA   = CUSPARSE_OPERATION_NON_TRANSPOSE;
    cusparseLtMatDescriptor_t mat_A;
    unsigned                  alignment = 16;
    CHECK_CUSPARSE(cusparseLtStructuredDescriptorInit(
        &cusparselt_handle_, &mat_A, m, k, m, alignment, CUDA_R_16F, order, CUSPARSELT_SPARSITY_50_PERCENT))
    CHECK_CUSPARSE(cusparseLtSpMMACompress2(&cusparselt_handle_, &mat_A, true, opA, input, output, stream_))
    sync_check_cuda_error();
}

bool cublasMMWrapper::isUseSparse(const int batch_count, const int m, const int n, const int k)
{
    return cublas_algo_map_->isUseSparse(batch_count, m, n, k);
}
#endif

std::pair<bool, cublasLtMatmulAlgo_t> cublasMMWrapper::findBestAlgo(cublasLtHandle_t       lightHandle,
                                                                    cublasLtMatmulDesc_t   computeDesc,
                                                                    const void*            alpha,
                                                                    const void*            A,
                                                                    cublasLtMatrixLayout_t Adesc,
                                                                    const void*            B,
                                                                    cublasLtMatrixLayout_t Bdesc,
                                                                    const void*            beta,
                                                                    const void*            C,
                                                                    cublasLtMatrixLayout_t Cdesc,
                                                                    void*                  D,
                                                                    cublasLtMatrixLayout_t Ddesc,
                                                                    cudaStream_t           stream)
{
//#if (CUBLAS_VERSION) <= 11601
    FT_CHECK_WITH_INFO(false, "CUBLAS version too low.");
    return {false, cublasLtMatmulAlgo_t{}};
/*#else
    size_t returnSize;
    int32_t pointer_mode;
    cublasLtMatmulDescGetAttribute(
        computeDesc, CUBLASLT_MATMUL_DESC_POINTER_MODE, &pointer_mode, sizeof(pointer_mode), &returnSize);

    std::vector<cublasLtMatmulHeuristicResult_t> heuristics(200);
    cublasLtMatmulPreference_t preference;
    check_cuda_error(cublasLtMatmulPreferenceCreate(&preference));
    check_cuda_error(cublasLtMatmulPreferenceInit(preference));
    uint64_t workspace_size = CUBLAS_WORKSPACE_SIZE;
    check_cuda_error(cublasLtMatmulPreferenceSetAttribute(
        preference, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workspace_size, sizeof(workspace_size)));
#if (CUBLAS_VERSION) <= 12000
    uint32_t pointer_mode_mask = 0;
    check_cuda_error(cublasLtMatmulPreferenceSetAttribute(
        preference, CUBLASLT_MATMUL_PREF_EPILOGUE_MASK, &pointer_mode_mask, sizeof(pointer_mode_mask)));
#endif

    int return_count = 0;
    auto ret = cublasLtMatmulAlgoGetHeuristic(lightHandle,
                                              computeDesc,
                                              Adesc,
                                              Bdesc,
                                              Cdesc,
                                              Ddesc,
                                              preference,
                                              heuristics.size(),
                                              heuristics.data(),
                                              &return_count);
    heuristics.resize(return_count);

    std::map<int, std::vector<float>> algo_results;
    for (const auto& heuristic : heuristics) {
        cublasLtMatmulAlgo_t algo = heuristic.algo;
        int32_t algo_id;
        cublasLtMatmulAlgoConfigGetAttribute(&algo, CUBLASLT_ALGO_CONFIG_ID, &algo_id, sizeof(algo_id), &returnSize);

        cudaEvent_t start_event, stop_event;
        cudaEventCreate(&start_event);
        cudaEventCreate(&stop_event);

        float my_alpha = 1.0f;
        float my_beta = 0.0f;

        for (int i = 0; i < 11; i++) {
            float duration_ms;
            cudaEventRecord(start_event, stream);
            check_cuda_error(cublasLtMatmul(lightHandle,
                                            computeDesc,
                                            alpha,
                                            A,
                                            Adesc,
                                            B,
                                            Bdesc,
                                            beta,
                                            C,
                                            Cdesc,
                                            D,
                                            Ddesc,
                                            &algo,
                                            cublas_workspace_,
                                            CUBLAS_WORKSPACE_SIZE,
                                            stream));
            cudaEventRecord(stop_event, stream);
            cudaEventSynchronize(stop_event);
            cudaEventElapsedTime(&duration_ms, start_event, stop_event);

            algo_results[algo_id].push_back(duration_ms);
        }
        std::sort(algo_results[algo_id].begin(), algo_results[algo_id].end());
    }

    cublasLtMatmulHeuristicResult_t result;
    float best_time = INFINITY;
    for (const auto& heuristic : heuristics) {
        cublasLtMatmulAlgo_t algo = heuristic.algo;
        int32_t algo_id;
        cublasLtMatmulAlgoConfigGetAttribute(&algo, CUBLASLT_ALGO_CONFIG_ID, &algo_id, sizeof(algo_id), &returnSize);
        const auto& results = algo_results[algo_id];

        if (results.size() > 0 && results[5] < best_time) {
            best_time = results[5];
            result = heuristic;
        }
    }

    return {best_time != INFINITY, result.algo};
#endif*/
}

cublasMMWrapper::MatrixLayout cublasMMWrapper::createMatrixLayout(cublasLtMatrixLayout_t Mdesc)
{
    size_t       returnSize;
    MatrixLayout m_layout;

    FT_CHECK_WITH_INFO(false, "cublasLtMatrixLayoutGetAttribute is not support.");
/*
    cublasLtMatrixLayoutGetAttribute(
        Mdesc, CUBLASLT_MATRIX_LAYOUT_TYPE, &std::get<0>(m_layout), sizeof(std::get<0>(m_layout)), &returnSize);
    cublasLtMatrixLayoutGetAttribute(
        Mdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &std::get<1>(m_layout), sizeof(std::get<1>(m_layout)), &returnSize);
    cublasLtMatrixLayoutGetAttribute(
        Mdesc, CUBLASLT_MATRIX_LAYOUT_ROWS, &std::get<2>(m_layout), sizeof(std::get<2>(m_layout)), &returnSize);
    cublasLtMatrixLayoutGetAttribute(
        Mdesc, CUBLASLT_MATRIX_LAYOUT_COLS, &std::get<3>(m_layout), sizeof(std::get<3>(m_layout)), &returnSize);
*/
    return m_layout;
}

cublasStatus_t cublasMMWrapper::cublasLtMatmulWrapper(cublasLtHandle_t            lightHandle,
                                                      cublasLtMatmulDesc_t        computeDesc,
                                                      const void*                 alpha,
                                                      const void*                 A,
                                                      cublasLtMatrixLayout_t      Adesc,
                                                      const void*                 B,
                                                      cublasLtMatrixLayout_t      Bdesc,
                                                      const void*                 beta,
                                                      const void*                 C,
                                                      cublasLtMatrixLayout_t      Cdesc,
                                                      void*                       D,
                                                      cublasLtMatrixLayout_t      Ddesc,
                                                      const cublasLtMatmulAlgo_t* algo,
                                                      void*                       workspace,
                                                      size_t                      workspaceSizeInBytes,
                                                      cudaStream_t                stream)
{
    cache_idx_t cache_idx{
        computeDesc,
        {createMatrixLayout(Adesc), createMatrixLayout(Bdesc), createMatrixLayout(Cdesc), createMatrixLayout(Ddesc)}};

    cublasLtMatmulAlgo_t algo_value;
    bool                 found_algo = false;
    if (algo == nullptr) {
        if (algo_cache.find(cache_idx) == algo_cache.end()) {
            auto result =
                findBestAlgo(lightHandle, computeDesc, alpha, A, Adesc, B, Bdesc, beta, C, Cdesc, D, Ddesc, stream);
            if (result.first) {
                algo_cache[cache_idx] = result.second;
                algo_value            = result.second;
                found_algo            = true;
            }
        }
        else {
            algo_value = algo_cache[cache_idx];
            found_algo = true;
        }
    }

    return cublasLtMatmul(lightHandle,
                          computeDesc,
                          alpha,
                          A,
                          Adesc,
                          B,
                          Bdesc,
                          beta,
                          C,
                          Cdesc,
                          D,
                          Ddesc,
                          found_algo ? &algo_value : algo,
                          workspace,
                          workspaceSizeInBytes,
                          stream);
}

void cublasMMWrapper::_Int8Gemm(const int     m,
                                const int     n,
                                const int     k,
                                const int8_t* A,
                                const int     lda,
                                const int8_t* B,
                                const int     ldb,
                                void*         C,
                                const int     ldc,
                                const void*   alpha,
                                const int     mode,
                                const bool    per_column_scaling)
{
    /* mode:
     *  - 0: int8 * int8 -> int32 -> int8
     *  - 1: int8 * int8 -> int32 -> int32
     */
// #if (CUBLAS_VERSION) <= 11601
#if 1
    FT_CHECK_WITH_INFO(false, "CUBLAS version too low.");
#else

    mu_->lock();
    const auto op_a = CUBLAS_OP_T;
    const auto op_b = CUBLAS_OP_N;
    const auto dataType = CUDA_R_8I;
    const auto resultType = mode == 0 ? CUDA_R_8I : CUDA_R_32I;
    const auto computeType = CUBLAS_COMPUTE_32I;
    const auto scaleType = mode == 0 ? CUDA_R_32F : CUDA_R_32I;
    const int batch_count = 1;
    const void* beta;

    int findAlgo = cublas_algo_map_->isExist(batch_count, m, n, k, getCublasDataType(dataType));

    cublasLtMatmulAlgo_info info = cublas_algo_map_->getAlgo(batch_count, m, n, k, getCublasDataType(dataType));

    cublasLtMatmulDesc_t operationDesc = NULL;
    cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;

    // --------------------------------------
    // Create descriptors for the original matrices
    check_cuda_error(cublasLtMatrixLayoutCreate(&Adesc, dataType, k, m, lda));
    check_cuda_error(cublasLtMatrixLayoutCreate(&Bdesc, dataType, k, n, ldb));
    check_cuda_error(cublasLtMatrixLayoutCreate(&Cdesc, resultType, m, n, ldc));

    check_cuda_error(cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType));

    auto pointer_mode = CUBLASLT_POINTER_MODE_HOST;
    if (mode == 0) {
        pointer_mode =
            per_column_scaling ? CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST : CUBLASLT_POINTER_MODE_DEVICE;
    }
    check_cuda_error(
        cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &op_a, sizeof(cublasOperation_t)));
    check_cuda_error(
        cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &op_b, sizeof(cublasOperation_t)));
    check_cuda_error(
        cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSC, &op_b, sizeof(cublasOperation_t)));
    check_cuda_error(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_POINTER_MODE, &pointer_mode, sizeof(pointer_mode)));

    const int32_t int_one = 1;
    const int32_t int_zero = 0;
    const float float_zero = 0;
    if (mode == 0) {
        beta = per_column_scaling ? &float_zero : NULL;
    }
    else {
        alpha = &int_one;
        beta = &int_zero;
    }

    cublasLtMatmulAlgo_t algo;
    void* workSpace = cublas_workspace_;
    int workspaceSize = cublas_workspace_ == NULL ? 0 : CUBLAS_WORKSPACE_SIZE;

    sync_check_cuda_error();
    auto ret = cublasLtMatmulWrapper(cublaslt_handle_,
                                     operationDesc,
                                     alpha,
                                     A,
                                     Adesc,
                                     B,
                                     Bdesc,
                                     beta,
                                     C,
                                     Cdesc,
                                     C,
                                     Cdesc,
                                     NULL,
                                     workSpace,
                                     workspaceSize,
                                     stream_);
    check_cuda_error(ret);
    sync_check_cuda_error();

    cublasLtMatmulDescDestroy(operationDesc);
    cublasLtMatrixLayoutDestroy(Adesc);
    cublasLtMatrixLayoutDestroy(Bdesc);
    cublasLtMatrixLayoutDestroy(Cdesc);
    sync_check_cuda_error();
    mu_->unlock();
#endif
}

void cublasMMWrapper::Int8Gemm(const int     m,
                               const int     n,
                               const int     k,
                               const int8_t* A,
                               const int     lda,
                               const int8_t* B,
                               const int     ldb,
                               int8_t*       C,
                               const int     ldc,
                               const float*  alpha,
                               const bool    per_column_scaling)
{
    return _Int8Gemm(m, n, k, A, lda, B, ldb, C, ldc, alpha, 0, per_column_scaling);
}

void cublasMMWrapper::Int8Gemm(const int     m,
                               const int     n,
                               const int     k,
                               const int8_t* A,
                               const int     lda,
                               const int8_t* B,
                               const int     ldb,
                               int32_t*      C,
                               const int     ldc)
{
    return _Int8Gemm(m, n, k, A, lda, B, ldb, C, ldc, (float*)nullptr, 1, false);
}

}  // namespace turbomind
