/*
 * Copyright (c) 2020-2023, 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 "src/turbomind/utils/gemm_test/encoder_gemm_func.h"
#include "src/turbomind/macro.h"
#include <chrono>

namespace turbomind {

template<typename T>
void generate_encoder_gemm_config(
    int batch_size, int seq_len, int head_num, int size_per_head, void* buffer_in, bool isAppend, int tensor_para_size)
{
    void* cublas_workspace;
    void* buffer;
    int   workSpaceSize;

#ifdef ENABLE_BF16
    if (std::is_same<T, half>::value || std::is_same<T, __nv_bfloat16>::value) {
#else
    if (std::is_same<T, half>::value) {
#endif  // ENABLE_BF16
        // cublas_workspace_ should be the start pointer of cudaMalloc()
        // to ensure 16B alignemnet
        cublas_workspace = buffer_in;
        buffer           = (void*)((char*)cublas_workspace + CUBLAS_WORKSPACE_SIZE);
        workSpaceSize    = CUBLAS_WORKSPACE_SIZE;
    }
    else {
        cublas_workspace = nullptr;
        buffer           = buffer_in;
        workSpaceSize    = 0;
    }

    struct cudaDeviceProp prop;
    check_cuda_error(cudaGetDeviceProperties(&prop, 0));
    printf("Device %s\n", prop.name);

    // check config
    FILE* fd;
    int   line_count = 0;
    if (!isAppend) {
        fd = fopen(GEMM_CONFIG, "w+");
    }
    else {
        fd = fopen(GEMM_CONFIG, "a+");
        std::vector<std::string> config;
        char                     line[1024];
        while (fgets(line, 1024, fd) != NULL) {
            config.push_back(std::string(line));
        }
        line_count = config.size();
        if (config.size() >= (MAX_CONFIG_NUM * GEMM_NUM + 1))  // 6 cublas/cublasLt, first row is not included
        {
            int startIdx = config.size() - ((MAX_CONFIG_NUM - 1) * GEMM_NUM);
            fclose(fd);
            fd = fopen(GEMM_CONFIG, "w+");
            fprintf(fd, "%s", config[0].c_str());
            for (uint i = startIdx; i < config.size(); i++) {
                fprintf(fd, "%s", config[i].c_str());
            }
            line_count = config.size() - (GEMM_NUM + 3);
        }
    }

    const int gemm_num = 7;
    int       M[gemm_num];
    int       N[gemm_num];
    int       K[gemm_num];
    int       batchCount[gemm_num] = {1, 1, 1, 1, 1, 1, 1};
    char      mess[gemm_num][256];
    float     exec_times[gemm_num];

    // gemm1
    M[0] = batch_size * seq_len;
    K[0] = head_num * size_per_head;
    N[0] = (head_num / tensor_para_size) * size_per_head;
    strcpy(mess[0], "from_tensor * weightQ/K/V");

    // gemm2
    M[1] = M[0];
    K[1] = head_num * size_per_head;
    N[1] = 4 * head_num * size_per_head / tensor_para_size;
    strcpy(mess[1], "attr_output * inter_kernel");

    // gemm3
    M[2] = M[0];
    K[2] = 4 * head_num * size_per_head / tensor_para_size;
    N[2] = head_num * size_per_head;
    strcpy(mess[2], "inter_matmul * output_kernel");

    M[3]          = seq_len;
    N[3]          = seq_len;
    K[3]          = size_per_head;
    batchCount[3] = batch_size * (head_num / tensor_para_size);
    strcpy(mess[3], "attention batched Gemm1");

    M[4]          = seq_len;
    N[4]          = size_per_head;
    K[4]          = seq_len;
    batchCount[4] = batch_size * (head_num / tensor_para_size);
    strcpy(mess[4], "attention batched Gemm2");

    M[5]          = batch_size * seq_len;
    N[5]          = (head_num / tensor_para_size) * size_per_head;
    K[5]          = head_num * size_per_head;
    batchCount[5] = 3;
    strcpy(mess[5], "from_tensor * weight_QKV in BatchGemm");

    M[6] = batch_size * seq_len;
    K[6] = (head_num / tensor_para_size) * size_per_head;
    N[6] = head_num * size_per_head;
    strcpy(mess[6], "attr * output_kernel");

    cublasHandle_t cublas_handle;
    check_cuda_error(cublasCreate(&cublas_handle));
    // cublasLtHandle_t ltHandle;
    // check_cuda_error(cublasLtCreate(&ltHandle));

    cudaDataType_t AType;
    cudaDataType_t BType;
    cudaDataType_t CType;
    cudaDataType_t computeType;
    int            startAlgo, endAlgo;
    const int      ites = 100;

    CublasDataType data_type;
    if (std::is_same<T, float>::value) {
        data_type   = FLOAT_DATATYPE;
        AType       = CUDA_R_32F;
        BType       = CUDA_R_32F;
        CType       = CUDA_R_32F;
        computeType = CUDA_R_32F;
        startAlgo   = (int)CUBLAS_GEMM_DEFAULT;
        // endAlgo     = (int)CUBLAS_GEMM_ALGO23;
        endAlgo     = (int)CUBLAS_GEMM_DEFAULT;
    }
    else if (std::is_same<T, half>::value) {
        data_type   = HALF_DATATYPE;
        AType       = CUDA_R_16F;
        BType       = CUDA_R_16F;
        CType       = CUDA_R_16F;
        computeType = CUDA_R_16F;
        // startAlgo   = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP;
        // endAlgo     = (int)CUBLAS_GEMM_ALGO15_TENSOR_OP;
        startAlgo   = (int)CUBLAS_GEMM_DEFAULT;
        endAlgo     = (int)CUBLAS_GEMM_DEFAULT;
    }
#ifdef ENABLE_BF16
    else if (std::is_same<T, __nv_bfloat16>::value) {
        data_type   = BFLOAT16_DATATYPE;
        AType       = CUDA_R_16BF;
        BType       = CUDA_R_16BF;
        CType       = CUDA_R_16BF;
        computeType = CUDA_R_32F;
        // startAlgo   = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP;
        // endAlgo     = (int)CUBLAS_GEMM_ALGO15_TENSOR_OP;
        startAlgo   = (int)CUBLAS_GEMM_DEFAULT;
        endAlgo     = (int)CUBLAS_GEMM_DEFAULT;
    }
#endif
    // using scaleT = typename ScaleTypeConverter<T, false>::Type;
    using scaleT = typename ScaleTypeConverter<T, true>::Type;

    scaleT alpha = (scaleT)1.0f;
    scaleT beta  = (scaleT)0.0f;

    printf("***Encoder Gemm Testing Begin***\n");
    printf("***Cublas Gemm Testing Begin***\n");
    if (line_count == 0) {
        fprintf(fd,
                "batch_size, seq_len, head_num, size_per_head dataType ### batchCount, n, m, k, algoId, "
                "customOption, tile, numSplitsK, swizzle, reductionScheme, workspaceSize, stages, exec_time\n");
    }
    for (int i = 0; i < gemm_num; ++i) {
        // if(i != 0 && i != 5) continue;

        int m = M[i], n = N[i], k = K[i];
        printf("\n-----------------------------\n");
        printf("GEMM test %d: [M: %d, K: %d, N: %d] %s\n", i, m, k, n, mess[i]);
        T* d_A = (T*)buffer;
        T* d_B = d_A + m * k * batchCount[i];
        T* d_C = d_B + k * n * batchCount[i];

        // array of pointer for batchedGemm
        T* harray[12];
        harray[0]  = (T*)buffer;
        harray[1]  = (T*)((char*)buffer + sizeof(T) * m * k);
        harray[2]  = (T*)((char*)buffer + 2 * sizeof(T) * m * k);
        harray[4]  = (T*)((char*)buffer + 3 * sizeof(T) * m * k);
        harray[5]  = (T*)((char*)buffer + 3 * sizeof(T) * m * k + sizeof(T) * k * n);
        harray[6]  = (T*)((char*)buffer + 3 * sizeof(T) * m * k + 2 * sizeof(T) * k * n);
        harray[8]  = (T*)((char*)buffer + 3 * sizeof(T) * m * k + 3 * sizeof(T) * k * n);
        harray[9]  = (T*)((char*)buffer + 3 * sizeof(T) * m * k + 3 * sizeof(T) * k * n + sizeof(T) * m * n);
        harray[10] = (T*)((char*)buffer + 3 * sizeof(T) * m * k + 3 * sizeof(T) * k * n + 2 * sizeof(T) * m * n);

        T** darray = 0;
        check_cuda_error(cudaMalloc((void**)&darray, sizeof(T*) * 12));
        cudaMemcpy((void*)darray, (void*)harray, sizeof(T*) * 12, cudaMemcpyHostToDevice);
        T** dAarray = darray;
        T** dBarray = darray + 4;
        T** dCarray = darray + 8;

        float exec_time = 99999.0f;
        int   fast_algo = 0;
        for (int algo = startAlgo; algo <= endAlgo; algo++) {
            cublasStatus_t status;
            cudaDeviceSynchronize();
            auto start = std::chrono::high_resolution_clock::now();
            for (int ite = 0; ite < ites; ++ite) {
                if (i < 3) {
                    status = cublasGemmEx(cublas_handle,
                                          CUBLAS_OP_N,
                                          CUBLAS_OP_N,
                                          n,
                                          m,
                                          k,
                                          &alpha,
                                          d_B,
                                          BType,
                                          n,
                                          d_A,
                                          AType,
                                          k,
                                          &beta,
                                          d_C,
                                          CType,
                                          n,
                                          computeType,
                                          static_cast<cublasGemmAlgo_t>(algo));
                }
                else if (i == 3) {
                    status = cublasGemmStridedBatchedEx(cublas_handle,
                                                        CUBLAS_OP_T,
                                                        CUBLAS_OP_N,
                                                        seq_len,
                                                        seq_len,
                                                        size_per_head,
                                                        &alpha,
                                                        d_B,
                                                        BType,
                                                        size_per_head,
                                                        seq_len * size_per_head,
                                                        d_A,
                                                        AType,
                                                        size_per_head,
                                                        seq_len * size_per_head,
                                                        &beta,
                                                        d_C,
                                                        CType,
                                                        seq_len,
                                                        seq_len * seq_len,
                                                        batch_size * head_num,
                                                        computeType,
                                                        static_cast<cublasGemmAlgo_t>(algo));
                }
                else if (i == 4) {
                    status = cublasGemmStridedBatchedEx(cublas_handle,
                                                        CUBLAS_OP_N,
                                                        CUBLAS_OP_N,
                                                        size_per_head,
                                                        seq_len,
                                                        seq_len,
                                                        &alpha,
                                                        d_B,
                                                        BType,
                                                        size_per_head,
                                                        seq_len * size_per_head,
                                                        d_A,
                                                        AType,
                                                        seq_len,
                                                        seq_len * seq_len,
                                                        &beta,
                                                        d_C,
                                                        CType,
                                                        size_per_head,
                                                        seq_len * size_per_head,
                                                        batch_size * head_num,
                                                        computeType,
                                                        static_cast<cublasGemmAlgo_t>(algo));
                }
                else if (i == 5) {
                    status = cublasGemmBatchedEx(cublas_handle,
                                                 CUBLAS_OP_N,
                                                 CUBLAS_OP_N,
                                                 n,
                                                 m,
                                                 k,
                                                 &alpha,
                                                 (const void* const*)dBarray,
                                                 BType,
                                                 n,
                                                 (const void* const*)dAarray,
                                                 AType,
                                                 k,
                                                 &beta,
                                                 (void* const*)dCarray,
                                                 CType,
                                                 n,
                                                 3,
                                                 computeType,
                                                 static_cast<cublasGemmAlgo_t>(algo));
                }
                if (status != CUBLAS_STATUS_SUCCESS) {
                    break;
                }
            }
            cudaDeviceSynchronize();
            auto end = std::chrono::high_resolution_clock::now();
            auto dur = std::chrono::duration<float, std::milli>(end - start);
            if (status == CUBLAS_STATUS_SUCCESS) {
                printf("algo_%d costs %.3fms \n", algo, dur.count() / ites);
                if (dur.count() / ites < exec_time) {
                    exec_time = dur.count() / ites;
                    fast_algo = algo;
                }
            }
        }
        printf("fast_algo %d costs %.3f ms\n", fast_algo, exec_time);

        // for fp16 and bf16, we compare cublasLt
        if (i < 3 && data_type != FLOAT_DATATYPE) {
            printf("***cublasLt Gemm Testing Begin***\n");
            // Let try a fixed number of combinations
            const int          ALGO_COMBINATIONS = 5000;
            customMatmulPerf_t perfResults[ALGO_COMBINATIONS];
            // LtHgemmCustomFind<T, scaleT>(ltHandle,
            //                              batch_size,
            //                              seq_len,
            //                              head_num,
            //                              size_per_head,
            //                              n,
            //                              m,
            //                              k,
            //                              &alpha,
            //                              d_B,
            //                              d_A,
            //                              &beta,
            //                              d_C,
            //                              cublas_workspace,
            //                              workSpaceSize,
            //                              fd,
            //                              perfResults,
            //                              ALGO_COMBINATIONS);
            // if (perfResults[0].time < exec_time) {
            //     printPerfStructure(
            //         batch_size, seq_len, head_num, size_per_head, n, m, k, perfResults[0], fd, data_type, 0);
            //     exec_time = perfResults[0].time;
            // }
            // else {
            {
                fprintf(fd,
                        "%d %d %d %d %d ### %d %d %d %d %d -1 -1 -1 -1 -1 -1 -1 "
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
                        "-1 -1 "
#elif (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH < 3)
                        "-1 -1 -1 "
#endif
                        "%f\n",
                        batch_size,
                        seq_len,
                        head_num,
                        size_per_head,
                        data_type,
                        batchCount[i],
                        n,
                        m,
                        k,
                        fast_algo,
                        exec_time);
            }
            printf("***cublasLt Gemm Testing End***\n");
        }
        else {
            fprintf(fd,
                    "%d %d %d %d %d ### %d %d %d %d %d -1 -1 -1 -1 -1 -1 -1 "
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
                    "-1 -1 "
#elif (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH < 3)
                    "-1 -1 -1 "
#endif
                    "%f\n",
                    batch_size,
                    seq_len,
                    head_num,
                    size_per_head,
                    data_type,
                    batchCount[i],
                    n,
                    m,
                    k,
                    fast_algo,
                    exec_time);
        }
        exec_times[i] = exec_time;
        cudaFree(darray);
    }
    printf("***cublas Gemm Testing End***\n\n");
    fclose(fd);
    printf("***Encoder Gemm Testing End***\n");

#ifdef SPARSITY_ENABLED
    bool do_sparse_test = false;
    if (prop.major == 8 && (prop.minor == 0 || prop.minor == 6)) {
        do_sparse_test = true;
    }
    if (do_sparse_test && sizeof(T) == sizeof(half)) {
        printf("***cusparseLt Gemm Testing Begin***\n");
        // only first 3 cases can be sparse
        const int spgemm_num = 3;
        if (!isAppend) {
            fd = fopen(SPGEMM_CONFIG, "w+");
        }
        else {
            fd = fopen(SPGEMM_CONFIG, "a+");
            std::vector<std::string> config;
            char                     line[1024];
            while (fgets(line, 1024, fd) != NULL) {
                config.push_back(std::string(line));
            }
            line_count = config.size();
            if (config.size() >= (MAX_CONFIG_NUM * spgemm_num + 1))  // 6 cublas/cublasLt, first row is not included
            {
                int startIdx = config.size() - ((MAX_CONFIG_NUM - 1) * spgemm_num);
                fclose(fd);
                fd = fopen(SPGEMM_CONFIG, "w+");
                fprintf(fd, "%s", config[0].c_str());
                for (uint i = startIdx; i < config.size(); i++) {
                    fprintf(fd, "%s", config[i].c_str());
                }
                line_count = config.size() - (spgemm_num + 3);
            }
        }
        if (line_count == 0) {
            fprintf(
                fd,
                "batch_size, seq_len, head_num, size_per_head dataType ### batchCount, m, n, k, algoId, exec_time\n");
        }
        cusparseLtHandle_t handle;
        CHECK_CUSPARSE(cusparseLtInit(&handle));
        cusparseOrder_t     order        = CUSPARSE_ORDER_COL;
        cusparseOperation_t opA          = CUSPARSE_OPERATION_NON_TRANSPOSE;
        cusparseOperation_t opB          = CUSPARSE_OPERATION_NON_TRANSPOSE;
        cusparseComputeType compute_type = CUSPARSE_COMPUTE_16F;
        unsigned            alignment    = 16;
        cudaStream_t        stream       = 0;
        float               alpha2       = 1.0f;
        float               beta2        = 0.0f;
        for (int i = 0; i < spgemm_num; ++i) {
            // to be compatible with spgemm wrapper, we let A be the weight matrix
            // so m and n are swapped
            // A: mxk B: kxn C:mxn
            int m = N[i], n = M[i], k = K[i];
            printf("\n-----------------------------\n");
            printf("GEMM test %d: [M: %d, K: %d, N: %d]\n", i, m, k, n);
            T* d_A = (T*)buffer;
            T* d_B = d_A + m * k * batchCount[i];
            T* d_C = d_B + k * n * batchCount[i];
            T* dA_compressed;
            {
                cusparseLtMatDescriptor_t mat_A;
                CHECK_CUSPARSE(cusparseLtStructuredDescriptorInit(
                    &handle, &mat_A, m, k, m, alignment, CUDA_R_16F, order, CUSPARSELT_SPARSITY_50_PERCENT))
                CHECK_CUSPARSE(
                    cusparseLtSpMMAPrune2(&handle, &mat_A, true, opA, d_A, d_A, CUSPARSELT_PRUNE_SPMMA_STRIP, stream))
                size_t compressed_size;
                CHECK_CUSPARSE(cusparseLtSpMMACompressedSize2(&handle, &mat_A, &compressed_size))
                check_cuda_error(cudaMalloc((void**)&dA_compressed, compressed_size));
                CHECK_CUSPARSE(cusparseLtSpMMACompress2(&handle, &mat_A, true, opA, d_A, dA_compressed, stream))
            }

            float exec_time = 99999.0f;
            int   fast_algo = 0;
            for (int alg = 0; alg < 4; ++alg) {
                cudaDeviceSynchronize();
                cusparseLtMatDescriptor_t mat_A, mat_B, mat_C;
                void*                     d_workspace = nullptr;
                int                       num_streams = 1;
                cudaStream_t              streams[1]  = {stream};
                CHECK_CUSPARSE(cusparseLtStructuredDescriptorInit(
                    &handle, &mat_A, m, k, m, alignment, CUDA_R_16F, order, CUSPARSELT_SPARSITY_50_PERCENT))
                CHECK_CUSPARSE(cusparseLtDenseDescriptorInit(&handle, &mat_B, k, n, k, alignment, CUDA_R_16F, order))
                CHECK_CUSPARSE(cusparseLtDenseDescriptorInit(&handle, &mat_C, m, n, m, alignment, CUDA_R_16F, order))
                auto start = std::chrono::high_resolution_clock::now();
                for (int ite = 0; ite < ites; ++ite) {
                    // initializing MatDesc takes a lot of time
                    // and these descs can be stored to other place
                    // whereas storing MatMulPlan to other place will cause errors
                    cusparseLtMatmulDescriptor_t   matmul;
                    cusparseLtMatmulAlgSelection_t alg_sel;
                    cusparseLtMatmulPlan_t         plan;
                    CHECK_CUSPARSE(cusparseLtMatmulDescriptorInit(
                        &handle, &matmul, opA, opB, &mat_A, &mat_B, &mat_C, &mat_C, compute_type))
                    CHECK_CUSPARSE(
                        cusparseLtMatmulAlgSelectionInit(&handle, &alg_sel, &matmul, CUSPARSELT_MATMUL_ALG_DEFAULT))
                    CHECK_CUSPARSE(cusparseLtMatmulAlgSetAttribute(
                        &handle, &alg_sel, CUSPARSELT_MATMUL_ALG_CONFIG_ID, &alg, sizeof(alg)))
                    size_t workspace_size;
                    CHECK_CUSPARSE(cusparseLtMatmulGetWorkspace(&handle, &alg_sel, &workspace_size))
                    CHECK_CUSPARSE(cusparseLtMatmulPlanInit(&handle, &plan, &matmul, &alg_sel, workspace_size))
                    CHECK_CUSPARSE(cusparseLtMatmul(&handle,
                                                    &plan,
                                                    &alpha2,
                                                    dA_compressed,
                                                    d_B,
                                                    &beta2,
                                                    d_C,
                                                    d_C,
                                                    d_workspace,
                                                    streams,
                                                    num_streams))
                    CHECK_CUSPARSE(cusparseLtMatmulPlanDestroy(&plan))
                }
                cudaDeviceSynchronize();
                auto end = std::chrono::high_resolution_clock::now();
                auto dur = std::chrono::duration<float, std::milli>(end - start);
                printf("algo_%d costs %.3fms \n", alg, dur.count() / ites);
                if (dur.count() < exec_time) {
                    exec_time = dur.count();
                    fast_algo = alg;
                }
            }
            exec_time /= ites;
            if (exec_time >= exec_times[i]) {
                fast_algo = -1;
            }
            printf("fast_algo %d\n", fast_algo);
            fprintf(fd,
                    "%d %d %d %d %d ### %d %d %d %d %d %f\n",
                    batch_size,
                    seq_len,
                    head_num,
                    size_per_head,
                    HALF_DATATYPE,
                    batchCount[i],
                    m,
                    n,
                    k,
                    fast_algo,
                    exec_time);
            cudaFree(dA_compressed);
        }
        CHECK_CUSPARSE(cusparseLtDestroy(&handle))
        fclose(fd);
        printf("***cusparseLt Gemm Testing End***\n");
    }
#endif
    return;
}

template void generate_encoder_gemm_config<float>(
    int batch_size, int seq_len, int head_num, int size_per_head, void* buffer, bool isAppend, int tensor_para_size);
template void generate_encoder_gemm_config<half>(
    int batch_size, int seq_len, int head_num, int size_per_head, void* buffer, bool isAppend, int tensor_para_size);
#ifdef ENABLE_BF16
template void generate_encoder_gemm_config<__nv_bfloat16>(
    int batch_size, int seq_len, int head_num, int size_per_head, void* buffer, bool isAppend, int tensor_para_size);
#endif

}  // namespace turbomind
