/*
 * Copyright (c) 2021-2022, 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 "3rdparty/INIReader.h"
#include "src/fastertransformer/models/multi_gpu_gpt/ParallelGpt.h"
#include "src/fastertransformer/utils/nvtx_utils.h"

#include <cuda_profiler_api.h>
#include <fstream>
#include <sstream>
#include <string>
#include <sys/time.h>
#include <vector>

#ifdef USE_NVTX
bool NVTX_ON = true;
#endif

using namespace fastertransformer;

template<typename T>
void gpt_example(const INIReader reader);

int main(int argc, char* argv[])
{
    srand(0);
    struct cudaDeviceProp prop;
    check_cuda_error(cudaGetDeviceProperties(&prop, 0));
    printf("Device %s\n", prop.name);

    std::string ini_name;
    if (argc == 2) {
        ini_name = std::string(argv[1]);
    }
    else {
        ini_name = "../examples/cpp/gpt/gpt_config.ini";
    }

    INIReader reader = INIReader(ini_name);
    if (reader.ParseError() < 0) {
        std::cout << "[ERROR] Can't load '" << ini_name << "'\n";
        return -1;
    }
    const std::string data_type = reader.Get("ft_instance_hyperparameter", "data_type");

    if (data_type == "fp32") {
        gpt_example<float>(reader);
    }
    else if (data_type == "fp16") {
        gpt_example<half>(reader);
    }
#ifdef ENABLE_BF16
    else if (data_type == "bf16") {
        gpt_example<__nv_bfloat16>(reader);
    }
#endif
    else {
        printf("[ERROR] data_type should be fp32, fp16 or bf16 ! \n");
        return -1;
    }
    return 0;
}

int read_start_ids(int batch_size,
                   std::vector<int>* v_start_lengths,
                   std::vector<int>* v_start_ids,
                   int& max_input_len,
                   const int end_id,
                   const int beam_width)
{
    std::vector<std::vector<int>> tmp_start_ids;
    std::vector<int> tmp_start_lengths;

    std::string file_name = "../examples/cpp/gpt/start_ids.csv";
    std::ifstream start_id_file(file_name, std::ios::in);
    if (start_id_file.is_open()) {
        std::string line;
        int i0 = 0;
        while (std::getline(start_id_file, line)) {
            std::stringstream lineStream(line);
            std::string vals;
            int i1 = 0;
            std::vector<int> tmp_vec;
            while (std::getline(lineStream, vals, ',')) {
                tmp_vec.push_back(std::stoi(vals));
                i1++;
            }
            tmp_start_ids.push_back(tmp_vec);
            tmp_start_lengths.push_back(i1);
            i0++;
        }
    }
    else {
        printf("[WARNING] Cannot open the file '%s'. \n", file_name.c_str());
        max_input_len = 0;
        return 0;
    }

    max_input_len = tmp_start_lengths.data()[0];
    for (uint i = 1; i < (uint)tmp_start_lengths.size(); i++) {
        max_input_len = max_input_len > tmp_start_lengths.data()[i] ? max_input_len : tmp_start_lengths.data()[i];
    }

    while ((int)tmp_start_lengths.size() < batch_size) {
        std::vector<int> padding_ids;
        for (int i = 0; i < max_input_len; i++) {
            padding_ids.push_back(end_id);
        }
        tmp_start_ids.push_back(padding_ids);
        tmp_start_lengths.push_back(max_input_len);
    }

    // Add padding
    for (int i = 0; i < (int)tmp_start_ids.size(); i++) {
        for (int j = (int)tmp_start_ids[i].size(); j < max_input_len; j++) {
            tmp_start_ids[i].push_back(end_id);
        }
    }

    for (int i = 0; i < (int)tmp_start_ids.size(); i++) {
        for (int b = 0; b < beam_width; b++) {
            for (int j = 0; j < (int)tmp_start_ids[i].size(); j++) {
                v_start_ids->push_back(tmp_start_ids[i][j]);
            }
            v_start_lengths->push_back(tmp_start_lengths[i]);
        }
    }

    return 0;
}

template<typename T>
void gpt_example(const INIReader reader)
{
    const std::string model_name = reader.Get("ft_instance_hyperparameter", "model_name");
    const size_t max_batch_size = reader.GetInteger("ft_instance_hyperparameter", "max_batch_size");
    const size_t max_seq_len = reader.GetInteger("ft_instance_hyperparameter", "max_seq_len");
    const size_t beam_width = reader.GetInteger("ft_instance_hyperparameter", "beam_width");
    const int top_k = reader.GetInteger("ft_instance_hyperparameter", "top_k");
    const float top_p = reader.GetFloat("ft_instance_hyperparameter", "top_p");
    const float temperature = reader.GetFloat("ft_instance_hyperparameter", "temperature");
    const float repetition_penalty = reader.GetFloat("ft_instance_hyperparameter", "repetition_penalty");
    const std::string model_dir = std::string(reader.Get("ft_instance_hyperparameter", "model_dir"));
    const bool sparse = static_cast<bool>(reader.GetInteger("ft_instance_hyperparameter", "sparse"));
    const float len_penalty = 1.0f;
    const float beam_search_diversity_rate = 0.0f;
    const unsigned long long int random_seed = 0;

    const size_t head_num = reader.GetInteger(model_name, "head_num");
    const size_t size_per_head = reader.GetInteger(model_name, "size_per_head");
    const size_t vocab_size = reader.GetInteger(model_name, "vocab_size");
    const size_t decoder_layers = reader.GetInteger(model_name, "decoder_layers");
    const size_t hidden_units = head_num * size_per_head;
    const size_t inter_size = 4 * hidden_units;

    const size_t request_batch_size = reader.GetInteger("request", "request_batch_size");
    // The length of tokens we hope this model to generate
    const int request_output_len = reader.GetInteger("request", "request_output_len");
    // Whether to return the log probabilities of outputs.
    const bool is_return_log_probs = reader.GetBoolean("request", "return_log_probs", false);
    // Whether to include input contexts in computing the cumulative log probabilities.
    const bool is_return_context_cum_log_probs = reader.GetBoolean("request", "context_log_probs", false);
    if (is_return_log_probs && !is_return_context_cum_log_probs) {
        FT_LOG_WARNING("context_log_probs will be ignored since return_log_probs is disabled.");
    }

    const int start_id = 50256;
    const int end_id = 50256;

    const int rank = 0;

    // Read ids of request from file.
    int max_input_len = -1;
    std::vector<int> v_start_lengths;
    std::vector<int> v_start_ids;
    read_start_ids(request_batch_size, &v_start_lengths, &v_start_ids, max_input_len, end_id, 1);

    int* d_input_ids;
    int* d_input_lengths;
    if (max_input_len == 0) {
        // unconditional case, no input ids, so do nothing.
        d_input_ids = nullptr;
        d_input_lengths = nullptr;
    }
    else {
        // conditional case.
        deviceMalloc(&d_input_ids, request_batch_size * max_input_len, false);
        deviceMalloc(&d_input_lengths, request_batch_size, false);
        cudaH2Dcpy(d_input_ids, v_start_ids.data(), request_batch_size * max_input_len);
        cudaH2Dcpy(d_input_lengths, v_start_lengths.data(), request_batch_size);
    }

    const int total_output_len = max_input_len + request_output_len;
    if (total_output_len > (int)max_seq_len) {
        printf("[ERROR] total_output_len (%d) should be <= max_seq_len (%ld). \n", total_output_len, max_seq_len);
        exit(-1);
    }

    cudaStream_t stream;
    cublasHandle_t cublas_handle;
    cublasLtHandle_t cublaslt_handle;
    cudaStreamCreate(&stream);
    cublasCreate(&cublas_handle);
    cublasLtCreate(&cublaslt_handle);
    cublasSetStream(cublas_handle, stream);
#ifdef SPARSITY_ENABLED
    cusparseLtHandle_t cusparselt_handle;
    CHECK_CUSPARSE(cusparseLtInit(&cusparselt_handle));
    cublasAlgoMap* cublas_algo_map = new cublasAlgoMap(GEMM_CONFIG, SPGEMM_CONFIG);
#else
    cublasAlgoMap* cublas_algo_map = new cublasAlgoMap(GEMM_CONFIG);
#endif

    Allocator<AllocatorType::CUDA> allocator(getDevice());

    std::mutex* cublas_wrapper_mutex = new std::mutex();
#ifdef SPARSITY_ENABLED
    cublasMMWrapper cublas_wrapper = cublasMMWrapper(
        cublas_handle, cublaslt_handle, cusparselt_handle, stream, cublas_algo_map, cublas_wrapper_mutex, &allocator);
#else
    cublasMMWrapper cublas_wrapper =
        cublasMMWrapper(cublas_handle, cublaslt_handle, stream, cublas_algo_map, cublas_wrapper_mutex, &allocator);
#endif

    if (std::is_same<T, half>::value) {
        cublas_wrapper.setGemmConfig(CUDA_R_16F, CUDA_R_16F, CUDA_R_16F, CUDA_R_32F);
    }
#ifdef ENABLE_BF16
    else if (std::is_same<T, __nv_bfloat16>::value) {
        cublas_wrapper.setBF16GemmConfig();
    }
#endif
    else if (std::is_same<T, float>::value) {
        cublas_wrapper.setFP32GemmConfig();
    }
    struct cudaDeviceProp prop;
    check_cuda_error(cudaGetDeviceProperties(&prop, 0));

    fastertransformer::ParallelGptWeight<T> gpt_weights(
        hidden_units, inter_size, vocab_size, decoder_layers, max_seq_len, 1, 0, 1, 0, 0);

    gpt_weights.loadModel(model_dir);

#ifdef SPARSITY_ENABLED
    if (sparse) {
        printf("[INFO] Compress weights for sparse inference\n");
        gpt_weights.compress_weights(cublas_wrapper);
    }
#endif
    NcclParam tensor_para;
    NcclParam pipeline_para;

    // TODO(bhsueh) Some parameters are move to runtime query.
    // Need to remove them in the future.
    ParallelGpt<T> gpt = ParallelGpt<T>(0,  // max_batch_size, FT will adjust the buffer automatically.
                                        0,  // max_seq_len, FT will adjust the buffer automatically.
                                        0,  // max_input_len, FT will adjust the buffer automatically.
                                        beam_width,
                                        head_num,
                                        size_per_head,
                                        inter_size,
                                        decoder_layers,
                                        vocab_size,
                                        start_id,
                                        end_id,
                                        0.0f,  // beam_search_diversity_rate,
                                        0,     // top_k,
                                        0.0,   // top_p,
                                        0,     // random_seed,
                                        1.0f,  // temperature,
                                        1.0f,  // len_penalty,
                                        1.0f,  // repetition_penalty,
                                        tensor_para,
                                        pipeline_para,
                                        stream,
                                        &cublas_wrapper,
                                        &allocator,
                                        false,
                                        &prop,
                                        sparse,
                                        0);

    int* d_output_ids;
    int* d_parent_ids;
    int* d_sequence_lengths;
    deviceMalloc(&d_output_ids, request_batch_size * beam_width * total_output_len, false);
    deviceMalloc(&d_parent_ids, request_batch_size * beam_width * total_output_len, false);
    deviceMalloc(&d_sequence_lengths, request_batch_size * beam_width, false);

    std::unordered_map<std::string, Tensor> input_tensors = std::unordered_map<std::string, Tensor>{
        {"input_ids",
         Tensor{MEMORY_GPU, TYPE_INT32, std::vector<size_t>{request_batch_size, (size_t)max_input_len}, d_input_ids}},
        {"input_lengths", Tensor{MEMORY_GPU, TYPE_INT32, std::vector<size_t>{request_batch_size}, d_input_lengths}},
        {"max_output_seq_len", Tensor{MEMORY_CPU, TYPE_INT32, std::vector<size_t>{1}, &total_output_len}},
        {"temperature", Tensor{MEMORY_CPU, TYPE_FP32, std::vector<size_t>{1}, &temperature}},
        {"len_penalty", Tensor{MEMORY_CPU, TYPE_FP32, std::vector<size_t>{1}, &len_penalty}},
        {"repetition_penalty", Tensor{MEMORY_CPU, TYPE_FP32, std::vector<size_t>{1}, &repetition_penalty}}};
    if (top_k == 0 && top_p == 0.0f) {
        FT_CHECK(beam_width > 1);
        input_tensors.insert({"beam_search_diversity_rate",
                              Tensor{MEMORY_CPU, TYPE_FP32, std::vector<size_t>{1}, &beam_search_diversity_rate}});
    }
    else {
        input_tensors.insert({"random_seed", Tensor{MEMORY_CPU, TYPE_UINT64, std::vector<size_t>{1}, &random_seed}});
        if (top_p != 0.0f) {
            input_tensors.insert({"runtime_top_p", Tensor{MEMORY_CPU, TYPE_FP32, std::vector<size_t>{1}, &top_p}});
        }
        if (top_k != 0) {
            input_tensors.insert({"runtime_top_k", Tensor{MEMORY_CPU, TYPE_INT32, std::vector<size_t>{1}, &top_k}});
        }
    }

    std::unordered_map<std::string, Tensor> output_tensors = std::unordered_map<std::string, Tensor>{
        {"output_ids",
         Tensor{MEMORY_GPU,
                TYPE_INT32,
                std::vector<size_t>{request_batch_size, beam_width, (size_t)total_output_len},
                d_output_ids}},
        {"parent_ids",
         Tensor{MEMORY_GPU,
                TYPE_INT32,
                std::vector<size_t>{(size_t)total_output_len, request_batch_size, beam_width},
                d_parent_ids}},
        {"sequence_length",
         Tensor{MEMORY_GPU, TYPE_INT32, std::vector<size_t>{request_batch_size, beam_width}, d_sequence_lengths}}};

    float* output_log_probs = nullptr;
    float* d_cum_log_probs = nullptr;
    if (is_return_log_probs) {
        deviceMalloc(&output_log_probs, request_batch_size * beam_width * request_output_len);
        output_tensors.insert({"output_log_probs",
                               Tensor{MEMORY_GPU,
                                      TYPE_FP32,
                                      std::vector<size_t>{request_batch_size, beam_width, (size_t)request_output_len},
                                      output_log_probs}});
        deviceMalloc(&d_cum_log_probs, request_batch_size * beam_width);
        output_tensors.insert(
            {"cum_log_probs",
             Tensor{MEMORY_GPU, TYPE_FP32, std::vector<size_t>{request_batch_size, beam_width}, d_cum_log_probs}});
        input_tensors.insert({"is_return_context_cum_log_probs",
                              Tensor{MEMORY_CPU, TYPE_BOOL, std::vector<size_t>{1}, &is_return_context_cum_log_probs}});
    }

    print_mem_usage();
    int ite = 1;
    cudaDeviceSynchronize();

    cudaProfilerStart();
    // warm up
    ite = 1;
    nvtx::setScope("warmup_time");
    //PUSH_RANGE("warmup time")
    for (int i = 0; i < ite; ++i) {
        gpt.forward(&output_tensors, &input_tensors, &gpt_weights);
    }
    cudaDeviceSynchronize();
    //POP_RANGE;
    nvtx::resetScope();

    struct timeval start, end;
    cudaDeviceSynchronize();
    gettimeofday(&start, NULL);

    nvtx::setScope("total_time");
    //PUSH_RANGE("total time")
    for (int i = 0; i < ite; ++i) {
        gpt.forward(&output_tensors, &input_tensors, &gpt_weights);
    }

    cudaDeviceSynchronize();
    //POP_RANGE;
    nvtx::resetScope();
    gettimeofday(&end, NULL);

    cudaProfilerStop();

    printf("[INFO] request_batch_size %ld beam_width %ld head_num %ld size_per_head %ld total_output_len %d"
           " decoder_layers %ld vocab_size %ld FT-CPP-decoding-beamsearch-time %.2f ms\n",
           request_batch_size,
           beam_width,
           head_num,
           size_per_head,
           total_output_len,
           decoder_layers,
           vocab_size,
           ((end.tv_sec - start.tv_sec) * 1000 + (end.tv_usec - start.tv_usec) * 0.001) / ite);

    if (rank == 0) {

        std::string fName = "out";
        auto outFile = std::ofstream(fName, std::ios::out);
        if (!outFile.is_open()) {
            printf("[WARNING] Cannot write results into output file %s \n", fName.c_str());
        }
        else {
            size_t outCount = total_output_len * request_batch_size * beam_width;
            int* hBuf = new int[outCount];
            cudaD2Hcpy(hBuf, d_output_ids, outCount);

            {
                std::cout << "Writing " << outCount << " elements\n";
                int zeroCount = 0;
                for (size_t i = 0; i < outCount; i++) {
                    if (hBuf[i] == int(0)) {
                        zeroCount++;
                    }
                    outFile << hBuf[i] << " ";
                    if ((i + 1) % (total_output_len) == 0) {
                        outFile << std::endl;
                    }

                    if (i < 10) {
                        printf("%5d ", hBuf[i]);
                    }
                    if ((i + 1) % (total_output_len) == 0 && i < 10) {
                        std::cout << std::endl;
                    }
                }
                std::cout << std::endl << "zeroCount = " << zeroCount << std::endl;
            }
            delete[] hBuf;
        }
        outFile.close();

        if (d_cum_log_probs != nullptr) {
            std::string logprob_fname = "logprob.out";
            std::ofstream logprob_file = std::ofstream("logprob.out", std::ios::out);
            if (!logprob_file.is_open()) {
                printf("[WARNING] Cannot write results into output file %s \n", logprob_fname.c_str());
            }
            else {
                size_t cum_log_probs_size = request_batch_size * beam_width;
                printf("[INFO] Writing %ld elements (log probs)\n", cum_log_probs_size);
                float* h_buf = new float[cum_log_probs_size];
                cudaD2Hcpy(h_buf, d_cum_log_probs, cum_log_probs_size);
                for (size_t i = 0; i < cum_log_probs_size; i++) {
                    logprob_file << h_buf[i] << std::endl;
                    if (i < 10) {
                        printf(" %10.6f\n", h_buf[i]);
                    }
                }
                delete[] h_buf;
            }
            logprob_file.close();
        }
    }

#ifdef SPARSITY_ENABLED
    cusparseLtDestroy(&cusparselt_handle);
#endif
    delete cublas_algo_map;
    delete cublas_wrapper_mutex;
    return;
}
