#include "ggml.h"

#include "utils.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>

#include <crow.h>
#include <json.hpp>

#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined(_WIN32)
#include <signal.h>
#endif

#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"

// determine number of model parts based on the dimension
static const std::map<int, int> LLAMA_N_PARTS = {
    {4096, 1},
    {5120, 2},
    {6656, 4},
    {8192, 8},
};

// default hparams (LLaMA 7B)
struct llama_hparams {
    int32_t n_vocab = 32000;
    int32_t n_ctx = 512; // this is provided as user input?
    int32_t n_embd = 4096;
    int32_t n_mult = 256;
    int32_t n_head = 32;
    int32_t n_layer = 32;
    int32_t n_rot = 64;
    int32_t f16 = 1;
};

struct llama_layer {
    // normalization
    struct ggml_tensor *attention_norm;

    // attention
    struct ggml_tensor *wq;
    struct ggml_tensor *wk;
    struct ggml_tensor *wv;
    struct ggml_tensor *wo;

    // normalization
    struct ggml_tensor *ffn_norm;

    // ff
    struct ggml_tensor *w1;
    struct ggml_tensor *w2;
    struct ggml_tensor *w3;
};

struct llama_model {
    llama_hparams hparams;

    struct ggml_tensor *tok_embeddings;

    struct ggml_tensor *norm;
    struct ggml_tensor *output;

    std::vector<llama_layer> layers;

    // key + value memory
    struct ggml_tensor *memory_k;
    struct ggml_tensor *memory_v;

    //
    struct ggml_context *ctx;
    std::map<std::string, struct ggml_tensor *> tensors;
};

// load the model's weights from a file
bool llama_model_load(const std::string &fname, llama_model &model,
                                            gpt_vocab &vocab, int n_ctx) {
    fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__,
                    fname.c_str());

    std::vector<char> f_buf(1024 * 1024);

    auto fin = std::ifstream(fname, std::ios::binary);
    fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
    if (!fin) {
        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
        return false;
    }

    // verify magic
    {
        uint32_t magic;
        fin.read((char *)&magic, sizeof(magic));
        if (magic != 0x67676d6c) {
            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__,
                            fname.c_str());
            return false;
        }
    }

    int n_ff = 0;
    int n_parts = 0;

    // load hparams
    {
        auto &hparams = model.hparams;

        fin.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
        // fin.read((char *) &hparams.n_ctx,     sizeof(hparams.n_ctx));
        fin.read((char *)&hparams.n_embd, sizeof(hparams.n_embd));
        fin.read((char *)&hparams.n_mult, sizeof(hparams.n_mult));
        fin.read((char *)&hparams.n_head, sizeof(hparams.n_head));
        fin.read((char *)&hparams.n_layer, sizeof(hparams.n_layer));
        fin.read((char *)&hparams.n_rot, sizeof(hparams.n_rot));
        fin.read((char *)&hparams.f16, sizeof(hparams.f16));

        hparams.n_ctx = n_ctx;

        n_ff =
                ((2 * (4 * hparams.n_embd) / 3 + hparams.n_mult - 1) / hparams.n_mult) *
                hparams.n_mult;
        n_parts = LLAMA_N_PARTS.at(hparams.n_embd);

        fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
        fprintf(stderr, "%s: n_ctx     = %d\n", __func__, hparams.n_ctx);
        fprintf(stderr, "%s: n_embd    = %d\n", __func__, hparams.n_embd);
        fprintf(stderr, "%s: n_mult    = %d\n", __func__, hparams.n_mult);
        fprintf(stderr, "%s: n_head    = %d\n", __func__, hparams.n_head);
        fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
        fprintf(stderr, "%s: n_rot     = %d\n", __func__, hparams.n_rot);
        fprintf(stderr, "%s: f16         = %d\n", __func__, hparams.f16);
        fprintf(stderr, "%s: n_ff        = %d\n", __func__, n_ff);
        fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
    }

    // load vocab
    {
        const int32_t n_vocab = model.hparams.n_vocab;

        if (n_vocab != model.hparams.n_vocab) {
            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
                            __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
            return false;
        }

        std::string word;
        for (int i = 0; i < n_vocab; i++) {
            uint32_t len;
            fin.read((char *)&len, sizeof(len));

            word.resize(len);
            fin.read((char *)word.data(), len);

            vocab.token_to_id[word] = i;
            vocab.id_to_token[i] = word;

            // if (i < 30000) {
            //        fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i,
            //        word.c_str());
            //}
        }
    }

    // for the big tensors, we have the option to store the data in 16-bit floats
    // or quantized in order to save memory and also to speed up the computation
    ggml_type wtype = GGML_TYPE_COUNT;
    switch (model.hparams.f16) {
    case 0:
        wtype = GGML_TYPE_F32;
        break;
    case 1:
        wtype = GGML_TYPE_F16;
        break;
    case 2:
        wtype = GGML_TYPE_Q4_0;
        break;
    case 3:
        wtype = GGML_TYPE_Q4_1;
        break;
    default: {
        fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
                        __func__, fname.c_str(), model.hparams.f16);
        return false;
    }
    }

    const ggml_type wtype2 = GGML_TYPE_F32;

    auto &ctx = model.ctx;

    size_t ctx_size = 0;

    {
        const auto &hparams = model.hparams;

        const int n_embd = hparams.n_embd;
        const int n_layer = hparams.n_layer;
        const int n_ctx = hparams.n_ctx;
        const int n_vocab = hparams.n_vocab;

        ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // tok_embeddings

        ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32); // norm

        ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // output

        ctx_size +=
                n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // attention_norm

        ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // wq
        ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // wk
        ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // wv
        ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // wo

        ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm

        ctx_size += n_layer * (n_ff * n_embd * ggml_type_sizef(wtype)); // w1
        ctx_size += n_layer * (n_ff * n_embd * ggml_type_sizef(wtype)); // w2
        ctx_size += n_layer * (n_ff * n_embd * ggml_type_sizef(wtype)); // w3

        ctx_size +=
                n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F32); // memory_k
        ctx_size +=
                n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F32); // memory_v

        ctx_size += (5 + 10 * n_layer) * 256; // object overhead

        fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__,
                        ctx_size / (1024.0 * 1024.0));
    }

    // create the ggml context
    {
        struct ggml_init_params params = {
                /*.mem_size     =*/ctx_size,
                /*.mem_buffer =*/NULL,
        };

        model.ctx = ggml_init(params);
        if (!model.ctx) {
            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
            return false;
        }
    }

    // prepare memory for the weights
    {
        const auto &hparams = model.hparams;

        const int n_embd = hparams.n_embd;
        const int n_layer = hparams.n_layer;
        const int n_ctx = hparams.n_ctx;
        const int n_vocab = hparams.n_vocab;

        model.layers.resize(n_layer);

        model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

        model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
        model.output = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

        // map by name
        model.tensors["tok_embeddings.weight"] = model.tok_embeddings;

        model.tensors["norm.weight"] = model.norm;
        model.tensors["output.weight"] = model.output;

        for (int i = 0; i < n_layer; ++i) {
            auto &layer = model.layers[i];

            layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
            layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

            layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

            layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
            layer.w2 = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
            layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);

            // map by name
            model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] =
                    layer.attention_norm;

            model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] =
                    layer.wq;
            model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] =
                    layer.wk;
            model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] =
                    layer.wv;
            model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] =
                    layer.wo;

            model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] =
                    layer.ffn_norm;

            model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] =
                    layer.w1;
            model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] =
                    layer.w2;
            model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] =
                    layer.w3;
        }
    }

    // key + value memory
    {
        const auto &hparams = model.hparams;

        const int n_embd = hparams.n_embd;
        const int n_layer = hparams.n_layer;
        const int n_ctx = hparams.n_ctx;

        const int n_mem = n_layer * n_ctx;
        const int n_elements = n_embd * n_mem;

        model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);

        const size_t memory_size =
                ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);

        fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__,
                        memory_size / 1024.0 / 1024.0, n_mem);
    }

    const size_t file_offset = fin.tellg();

    fin.close();

    std::vector<uint8_t> tmp;

    for (int i = 0; i < n_parts; ++i) {
        const int part_id = i;
        // const int part_id = n_parts - i - 1;

        std::string fname_part = fname;
        if (i > 0) {
            fname_part += "." + std::to_string(i);
        }

        fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i + 1,
                        n_parts, fname_part.c_str());

        fin = std::ifstream(fname_part, std::ios::binary);
        fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
        fin.seekg(file_offset);

        // load weights
        {
            int n_tensors = 0;
            size_t total_size = 0;

            fprintf(stderr, "%s: ", __func__);

            while (true) {
                int32_t n_dims;
                int32_t length;
                int32_t ftype;

                fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
                fin.read(reinterpret_cast<char *>(&length), sizeof(length));
                fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));

                if (fin.eof()) {
                    break;
                }

                int32_t nelements = 1;
                int32_t ne[2] = {1, 1};
                for (int i = 0; i < n_dims; ++i) {
                    fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
                    nelements *= ne[i];
                }

                std::string name(length, 0);
                fin.read(&name[0], length);

                if (model.tensors.find(name.data()) == model.tensors.end()) {
                    fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__,
                                    name.data());
                    return false;
                }

                // split_type = 0: split by columns
                // split_type = 1: split by rows
                int split_type = 0;

                // split_type = 0:
                // regex:
                //     - tok_embeddings.*
                //     - layers.*.attention.wo.weight
                //     - layers.*.feed_forward.w2.weight

                // split_type = 1:
                // regex:
                //     - output.*
                //     - layers.*.attention.wq.weight
                //     - layers.*.attention.wk.weight
                //     - layers.*.attention.wv.weight
                //     - layers.*.feed_forward.w1.weight
                //     - layers.*.feed_forward.w3.weight
                if (name.find("tok_embeddings") != std::string::npos) {
                    split_type = 0;
                } else if (name.find("layers") != std::string::npos) {
                    if (name.find("attention.wo.weight") != std::string::npos) {
                        split_type = 0;
                    } else if (name.find("feed_forward.w2.weight") != std::string::npos) {
                        split_type = 0;
                    } else {
                        split_type = 1;
                    }
                } else if (name.find("output") != std::string::npos) {
                    split_type = 1;
                }

                auto tensor = model.tensors[name.data()];

                if (n_dims == 1) {
                    if (ggml_nelements(tensor) != nelements) {
                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n",
                                        __func__, name.data());
                        return false;
                    }
                } else {
                    if (ggml_nelements(tensor) / n_parts != nelements) {
                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n",
                                        __func__, name.data());
                        return false;
                    }
                }

                if (n_dims == 1) {
                    if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
                        fprintf(stderr,
                                        "%s: tensor '%s' has wrong shape in model file: got [%d, "
                                        "%d], expected [%d, %d]\n",
                                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0],
                                        ne[1]);
                        return false;
                    }
                } else {
                    if (split_type == 0) {
                        if (tensor->ne[0] / n_parts != ne[0] || tensor->ne[1] != ne[1]) {
                            fprintf(stderr,
                                            "%s: tensor '%s' has wrong shape in model file: got [%d, "
                                            "%d], expected [%d, %d]\n",
                                            __func__, name.data(), tensor->ne[0] / n_parts,
                                            tensor->ne[1], ne[0], ne[1]);
                            return false;
                        }
                    } else {
                        if (tensor->ne[0] != ne[0] || tensor->ne[1] / n_parts != ne[1]) {
                            fprintf(stderr,
                                            "%s: tensor '%s' has wrong shape in model file: got [%d, "
                                            "%d], expected [%d, %d]\n",
                                            __func__, name.data(), tensor->ne[0],
                                            tensor->ne[1] / n_parts, ne[0], ne[1]);
                            return false;
                        }
                    }
                }

                if (0) {
                    static const char *ftype_str[] = {
                            "f32",
                            "f16",
                            "q4_0",
                            "q4_1",
                    };
                    fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n",
                                    name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
                }

                size_t bpe = 0;

                switch (ftype) {
                case 0:
                    bpe = ggml_type_size(GGML_TYPE_F32);
                    break;
                case 1:
                    bpe = ggml_type_size(GGML_TYPE_F16);
                    break;
                case 2:
                    bpe = ggml_type_size(GGML_TYPE_Q4_0);
                    assert(ne[0] % 64 == 0);
                    break;
                case 3:
                    bpe = ggml_type_size(GGML_TYPE_Q4_1);
                    assert(ne[0] % 64 == 0);
                    break;
                default: {
                    fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__,
                                    ftype);
                    return false;
                }
                };

                if (n_dims == 1 || n_parts == 1) {
                    if ((nelements * bpe) / ggml_blck_size(tensor->type) !=
                            ggml_nbytes(tensor)) {
                        fprintf(stderr,
                                        "%s: tensor '%s' has wrong size in model file: got %zu, "
                                        "expected %zu\n",
                                        __func__, name.data(), ggml_nbytes(tensor),
                                        nelements * bpe);
                        return false;
                    }

                    if (part_id == 0) {
                        fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
                    } else {
                        fin.seekg(ggml_nbytes(tensor), std::ios::cur);
                    }

                    total_size += ggml_nbytes(tensor);
                } else {
                    if ((nelements * bpe) / ggml_blck_size(tensor->type) !=
                            ggml_nbytes(tensor) / n_parts) {
                        fprintf(stderr,
                                        "%s: tensor '%s' has wrong size in model file: got %zu, "
                                        "expected %zu\n",
                                        __func__, name.data(), ggml_nbytes(tensor) / n_parts,
                                        nelements * bpe);
                        return false;
                    }

                    if (split_type == 0) {
                        const int np0 = ne[0];

                        const size_t row_size =
                                (tensor->ne[0] / ggml_blck_size(tensor->type)) *
                                ggml_type_size(tensor->type);
                        assert(row_size == tensor->nb[1]);

                        for (int i1 = 0; i1 < ne[1]; ++i1) {
                            const size_t offset_row = i1 * row_size;
                            const size_t offset =
                                    offset_row +
                                    ((part_id * np0) / ggml_blck_size(tensor->type)) *
                                            ggml_type_size(tensor->type);
                            fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size / n_parts);
                        }
                    } else {
                        const int np1 = ne[1];

                        const size_t row_size =
                                (tensor->ne[0] / ggml_blck_size(tensor->type)) *
                                ggml_type_size(tensor->type);

                        for (int i1 = 0; i1 < ne[1]; ++i1) {
                            const size_t offset_row = (i1 + part_id * np1) * row_size;
                            fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
                        }
                    }

                    total_size += ggml_nbytes(tensor) / n_parts;
                }

                // fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n",
                // name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16",
                // ggml_nbytes(tensor)/1024.0/1024.0);
                if (++n_tensors % 8 == 0) {
                    fprintf(stderr, ".");
                    fflush(stderr);
                }
            }

            fprintf(stderr, " done\n");

            fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n",
                            __func__, total_size / 1024.0 / 1024.0, n_tensors);
        }

        fin.close();
    }

    return true;
}

// evaluate the transformer
//
//     - model:         the model
//     - n_threads: number of threads to use
//     - n_past:        the context size so far
//     - embd_inp:    the embeddings of the tokens in the context
//     - embd_w:        the predicted logits for the next token
//
// The GPT-J model requires about 16MB of memory per input token.
//
bool llama_eval(const llama_model &model, const int n_threads, const int n_past,
                                const std::vector<gpt_vocab::id> &embd_inp,
                                std::vector<float> &embd_w, size_t &mem_per_token) {
    const int N = embd_inp.size();

    const auto &hparams = model.hparams;

    const int n_embd = hparams.n_embd;
    const int n_layer = hparams.n_layer;
    const int n_ctx = hparams.n_ctx;
    const int n_head = hparams.n_head;
    const int n_vocab = hparams.n_vocab;
    const int n_rot = hparams.n_embd / hparams.n_head;

    const int d_key = n_embd / n_head;

    // TODO: check if this size scales with n_ctx linearly and remove constant.
    // somehow I feel it wasn't the case
    // static size_t buf_size = hparams.n_ctx*1024*1024;
    static size_t buf_size = 512u * 1024 * 1024;
    static void *buf = malloc(buf_size);

    if (mem_per_token > 0 && mem_per_token * N > buf_size) {
        const size_t buf_size_new =
                1.1 *
                (mem_per_token * N); // add 10% to account for ggml object overhead
        // fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n",
        // __func__, buf_size, buf_size_new);

        // reallocate
        buf_size = buf_size_new;
        buf = realloc(buf, buf_size);
        if (buf == nullptr) {
            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
            return false;
        }
    }

    struct ggml_init_params params = {
            /*.mem_size     =*/buf_size,
            /*.mem_buffer =*/buf,
    };

    struct ggml_context *ctx0 = ggml_init(params);
    ggml_cgraph gf = {};
    gf.n_threads = n_threads;

    struct ggml_tensor *embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
    memcpy(embd->data, embd_inp.data(), N * ggml_element_size(embd));

    struct ggml_tensor *inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);

    for (int il = 0; il < n_layer; ++il) {
        struct ggml_tensor *inpSA = inpL;

        struct ggml_tensor *cur;

        // norm
        {
            cur = ggml_rms_norm(ctx0, inpL);

            // cur = attention_norm*cur
            cur = ggml_mul(
                    ctx0, ggml_repeat(ctx0, model.layers[il].attention_norm, cur), cur);
        }

        // self-attention
        {
            struct ggml_tensor *Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
            struct ggml_tensor *Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
            struct ggml_tensor *Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);

            // store key and value to memory
            if (N >= 1) {
                struct ggml_tensor *k =
                        ggml_view_1d(ctx0, model.memory_k, N * n_embd,
                                                 (ggml_element_size(model.memory_k) * n_embd) *
                                                         (il * n_ctx + n_past));
                struct ggml_tensor *v =
                        ggml_view_1d(ctx0, model.memory_v, N * n_embd,
                                                 (ggml_element_size(model.memory_v) * n_embd) *
                                                         (il * n_ctx + n_past));

                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
            }

            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1,
            // 3)
            struct ggml_tensor *Q = ggml_permute(ctx0, ggml_rope(ctx0, ggml_cpy(ctx0, Qcur, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd / n_head, n_head, N)), n_past, n_rot, 0), 0, 2, 1, 3);

            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
            struct ggml_tensor *K = ggml_permute(ctx0, ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_view_1d(ctx0, model.memory_k, (n_past + N) * n_embd, il * n_ctx * ggml_element_size(model.memory_k) *  n_embd), n_embd / n_head, n_head, n_past + N), n_past, n_rot, 1), 0, 2, 1, 3);

            // K * Q
            struct ggml_tensor *KQ = ggml_mul_mat(ctx0, K, Q);

            // KQ_scaled = KQ / sqrt(n_embd/n_head)
            struct ggml_tensor *KQ_scaled = ggml_scale(ctx0, KQ, ggml_new_f32(ctx0, 1.0f / sqrt(float(n_embd) / n_head)));

            // KQ_masked = mask_past(KQ_scaled)
            struct ggml_tensor *KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);

            // KQ = soft_max(KQ_masked)
            struct ggml_tensor *KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);

            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0,
            // 3).contiguous()
            struct ggml_tensor *V_trans = ggml_permute(
                    ctx0,
                    ggml_reshape_3d(
                            ctx0,
                            ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_embd, il * n_ctx * ggml_element_size(model.memory_v) * n_embd),
                            n_embd / n_head, n_head, n_past + N),
                    1, 2, 0, 3);

            // KQV = transpose(V) * KQ_soft_max
            struct ggml_tensor *KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);

            // KQV_merged = KQV.permute(0, 2, 1, 3)
            struct ggml_tensor *KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

            // cur = KQV_merged.contiguous().view(n_embd, N)
            cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));

            // projection (no bias)
            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
        }

        struct ggml_tensor *inpFF = ggml_add(ctx0, cur, inpSA);

        // feed-forward network
        {
            // norm
            {
                cur = ggml_rms_norm(ctx0, inpFF);

                // cur = ffn_norm*cur
                cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ffn_norm, cur), cur);
            }

            struct ggml_tensor *tmp = ggml_mul_mat(ctx0, model.layers[il].w3, cur);

            cur = ggml_mul_mat(ctx0, model.layers[il].w1, cur);

            // SILU activation
            cur = ggml_silu(ctx0, cur);

            cur = ggml_mul(ctx0, cur, tmp);

            cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
        }

        cur = ggml_add(ctx0, cur, inpFF);

        // input for next layer
        inpL = cur;
    }

    // norm
    {
        inpL = ggml_rms_norm(ctx0, inpL);

        // inpL = norm*inpL
        inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.norm, inpL), inpL);
    }

    // lm_head
    { inpL = ggml_mul_mat(ctx0, model.output, inpL); }

    // logits -> probs
    // inpL = ggml_soft_max(ctx0, inpL);

    // run the computation
    ggml_build_forward_expand(&gf, inpL);
    ggml_graph_compute(ctx0, &gf);

    // if (n_past%100 == 0) {
    //        ggml_graph_print     (&gf);
    //        ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
    //}

    // embd_w.resize(n_vocab*N);
    // memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);

    // return result for just the last token
    embd_w.resize(n_vocab);
    memcpy(embd_w.data(), (float *)ggml_get_data(inpL) + (n_vocab * (N - 1)),
                 sizeof(float) * n_vocab);

    if (mem_per_token == 0) {
        mem_per_token = ggml_used_mem(ctx0) / N;
    }
    // fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}

static bool is_interacting = false;

#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) ||                    \
        defined(_WIN32)
void sigint_handler(int signo) {
    printf(ANSI_COLOR_RESET);
    if (signo == SIGINT) {
        if (!is_interacting) {
            is_interacting = true;
        } else {
            _exit(130);
        }
    }
}
#endif

const char *llama_print_system_info(void) {
    static std::string s;

    s = "";
    s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
    s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
    s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
    s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
    s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
    s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
    s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
    s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
    s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
    s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
    s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
    s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";

    return s.c_str();
}

int main(int argc, char **argv) {
    ggml_time_init();
    const int64_t t_main_start_us = ggml_time_us();

    gpt_params params;
    params.model = "models/7B/ggml-model.bin";

    if (gpt_params_parse(argc, argv, params) == false) {
        return 1;
    }

    if (params.seed < 0) {
        params.seed = time(NULL);
    }

    fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);

    std::mt19937 rng(params.seed);
    if (params.prompt.empty()) {
        params.prompt = gpt_random_prompt(rng);
    }

    //        params.prompt = R"(// this function checks if the number n is prime
    // bool is_prime(int n) {)";

    int64_t t_load_us = 0;

    gpt_vocab vocab;
    llama_model model;

    // load the model
    {
        const int64_t t_start_us = ggml_time_us();
        if (!llama_model_load(params.model, model, vocab, params.n_ctx)) {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__,
                            params.model.c_str());
            return 1;
        }

        t_load_us = ggml_time_us() - t_start_us;
    }

    // print system information
    {
        fprintf(stderr, "\n");
        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads,
                        std::thread::hardware_concurrency(), llama_print_system_info());
    }

    printf("Initializing crow...");

    crow::SimpleApp app;

    CROW_ROUTE(app, "/")([&](const crow::request& req) -> std::string {
        nlohmann::json json_data = nlohmann::json::parse(req.body);
        std::string input = "Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\n";
        
        auto messages = json_data["messages"];

        for (auto& message : messages) {
            std::string role = message["role"];
            std::string content = message["content"];

            if (role == "user") {
                input = input + "\nUser: " + content;
            } else {
                input = input + "\nBob: " + content;
            }
        }

        input = input + "\nBob: ";

        std::string result = "";

        int n_past = 0;

        int64_t t_sample_us = 0;
        int64_t t_predict_us = 0;

        std::vector<float> logits;

        // tokenize the prompt
        std::vector<gpt_vocab::id> embd_inp =
                ::llama_tokenize(vocab, input, true);

        params.n_predict =
                std::min(params.n_predict, model.hparams.n_ctx - (int)embd_inp.size());

        // tokenize the reverse prompt
        std::vector<gpt_vocab::id> antiprompt_inp =
                ::llama_tokenize(vocab, params.antiprompt, false);

        fprintf(stderr, "\n");
        fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
        fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__,
                        embd_inp.size());
        for (int i = 0; i < (int)embd_inp.size(); i++) {
            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i],
                            vocab.id_to_token.at(embd_inp[i]).c_str());
        }
        fprintf(stderr, "\n");
        if (params.interactive) {
    #if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
            struct sigaction sigint_action;
            sigint_action.sa_handler = sigint_handler;
            sigemptyset(&sigint_action.sa_mask);
            sigint_action.sa_flags = 0;
            sigaction(SIGINT, &sigint_action, NULL);
    #elif defined(_WIN32)
            signal(SIGINT, sigint_handler);
    #endif

            fprintf(stderr, "%s: interactive mode on.\n", __func__);

            if (antiprompt_inp.size()) {
                fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__,
                                params.antiprompt.c_str());
                fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n",
                                __func__, antiprompt_inp.size());
                for (int i = 0; i < (int)antiprompt_inp.size(); i++) {
                    fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i],
                                    vocab.id_to_token.at(antiprompt_inp[i]).c_str());
                }
                fprintf(stderr, "\n");
            }
        }
        fprintf(stderr,
                        "sampling parameters: temp = %f, top_k = %d, top_p = %f, "
                        "repeat_last_n = %i, repeat_penalty = %f\n",
                        params.temp, params.top_k, params.top_p, params.repeat_last_n,
                        params.repeat_penalty);
        fprintf(stderr, "\n\n");

        std::vector<gpt_vocab::id> embd;

        // determine the required inference memory per token:
        size_t mem_per_token = 0;
        llama_eval(model, params.n_threads, 0, {0, 1, 2, 3}, logits, mem_per_token);

        int last_n_size = params.repeat_last_n;
        std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
        std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);

        if (params.interactive) {
            fprintf(stderr,
                            "== Running in interactive mode. ==\n"
    #if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) ||                    \
            defined(_WIN32)
                            " - Press Ctrl+C to interject at any time.\n"
    #endif
                            " - Press Return to return control to LLaMa.\n"
                            " - If you want to submit another line, end your input in '\\'.\n");
        }

        int remaining_tokens = params.n_predict;
        int input_consumed = 0;
        bool input_noecho = false;

        // prompt user immediately after the starting prompt has been loaded
        if (params.interactive_start) {
            is_interacting = true;
        }

        // set the color for the prompt which will be output initially
        if (params.use_color) {
            printf(ANSI_COLOR_YELLOW);
        }

        while (remaining_tokens > 0) {
            // predict
            if (embd.size() > 0) {
                const int64_t t_start_us = ggml_time_us();

                if (!llama_eval(model, params.n_threads, n_past, embd, logits,
                                                mem_per_token)) {
                    fprintf(stderr, "Failed to predict\n");
                    return std::string("Failed to predict");
                }

                t_predict_us += ggml_time_us() - t_start_us;
            }

            n_past += embd.size();
            embd.clear();

            if (embd_inp.size() <= input_consumed) {
                // out of user input, sample next token
                const float top_k = params.top_k;
                const float top_p = params.top_p;
                const float temp = params.temp;
                const float repeat_penalty = params.repeat_penalty;

                const int n_vocab = model.hparams.n_vocab;

                gpt_vocab::id id = 0;

                {
                    const int64_t t_start_sample_us = ggml_time_us();

                    id = llama_sample_top_p_top_k(
                            vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens,
                            repeat_penalty, top_k, top_p, temp, rng);

                    last_n_tokens.erase(last_n_tokens.begin());
                    last_n_tokens.push_back(id);

                    t_sample_us += ggml_time_us() - t_start_sample_us;
                }

                // add it to the context
                embd.push_back(id);

                // echo this to console
                input_noecho = false;
            } else {
                // some user input remains from prompt or interaction, forward it to
                // processing
                while (embd_inp.size() > input_consumed) {
                    embd.push_back(embd_inp[input_consumed]);
                    last_n_tokens.erase(last_n_tokens.begin());
                    last_n_tokens.push_back(embd_inp[input_consumed]);
                    ++input_consumed;
                    if (embd.size() > params.n_batch) {
                        break;
                    }
                }

                // reset color to default if we there is no pending user input
                if (!input_noecho && params.use_color &&
                        embd_inp.size() == input_consumed) {
                    printf(ANSI_COLOR_RESET);
                }
            }

            // in interactive mode, and not currently processing queued inputs;
            // check if we should prompt the user for more
            if (params.interactive && embd_inp.size() <= input_consumed) {
                // check for reverse prompt
                if (antiprompt_inp.size() &&
                        std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(),
                                            last_n_tokens.rbegin())) {
                    // reverse prompt found
                    printf("Reverse prompt found.\n");
                    result.erase(result.size() - 4);
                    is_interacting = true;
                }
                if (is_interacting) {
                    is_interacting = false;
                    break;
                }
            }

            // display text
            if (!input_noecho) {
                for (auto id : embd) {
                    result = result + vocab.id_to_token[id].c_str();
                    printf("%s", vocab.id_to_token[id].c_str());
                }
                fflush(stdout);
            }

            // end of text token
            if (embd.back() == 2) {
                fprintf(stderr, " [end of text]\n");
                break;
            }
        }

    #if defined(_WIN32)
        signal(SIGINT, SIG_DFL);
    #endif

        // report timing
        {
                const int64_t t_main_end_us = ggml_time_us();

            fprintf(stderr, "\n\n");
            fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__,
                            mem_per_token);
            fprintf(stderr, "%s:         load time = %8.2f ms\n", __func__,
                            t_load_us / 1000.0f);
            fprintf(stderr, "%s:     sample time = %8.2f ms\n", __func__,
                            t_sample_us / 1000.0f);
            fprintf(stderr, "%s:    predict time = %8.2f ms / %.2f ms per token\n",
                            __func__, t_predict_us / 1000.0f, t_predict_us / 1000.0f / n_past);
            fprintf(stderr, "%s:        total time = %8.2f ms\n", __func__,
                            (t_main_end_us - t_main_start_us) / 1000.0f);
        }

        return result.substr(input.length());
    });

    app.port(18080).multithreaded().run();

    ggml_free(model.ctx);

    if (params.use_color) {
        printf(ANSI_COLOR_RESET);
    }

    return 0;
}