main-ctx.cpp

#include "ggml.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <thread>
#include <ctime>
#include <random>
#include <regex>

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

// default hparams (GPT-2 774M)
struct gpt_hparams {
    int32_t n_vocab = 50257;   // Vocabulary size remains the same
    //int32_t n_ctx   = 1024;     // Maximum context length (sequence length)
    int32_t n_embd  = 1024;     // Embedding dimensionality
    int32_t n_head  = 16;       // Number of attention heads
    int32_t n_layer = 24;       // Number of transformer layers
    int32_t ftype   = 1;        // Set to 1 for FP16 precision (optional)
    float   eps     = 1e-5f;    // Small constant for numerical stability

    int32_t seed         = -1;   // RNG seed
    int32_t n_threads    = std::min(4, (int32_t) std::thread::hardware_concurrency());
    int32_t n_predict    = 200;  // new tokens to predict
    int32_t n_parallel   = 1;    // number of parallel streams
    int32_t n_batch      = 32;   // batch size for prompt processing
    int32_t n_ctx        = 2048; // context size (this is the KV cache max size)
    int32_t n_gpu_layers = 0;    // number of layers to offlload to the GPU

    bool ignore_eos = false; // ignore EOS token when generating text

    // sampling parameters
    int32_t top_k          = 40;
    float   top_p          = 0.9f;
    float   temp           = 0.9f;
    int32_t repeat_last_n  = 64;
    float   repeat_penalty = 1.00f;

    std::string model      = "ggml-model-gpt-2-774M.bin"; // model path
    std::string prompt     = "";
    std::string token_test = "";

    bool    interactive      = false;
    int32_t interactive_port = -1;
};

struct gpt_vocab {
    using id    = int32_t;
    using token = std::string;

    std::map<token, id> token_to_id;
    std::map<id, token> id_to_token;
    std::vector<std::string> special_tokens;

    void add_special_token(const std::string & token);
};

struct gpt_layer {
    // normalization
    struct ggml_tensor * ln_1_g;
    struct ggml_tensor * ln_1_b;

    struct ggml_tensor * ln_2_g;
    struct ggml_tensor * ln_2_b;

    // attention
    struct ggml_tensor * c_attn_attn_w;
    struct ggml_tensor * c_attn_attn_b;

    struct ggml_tensor * c_attn_proj_w;
    struct ggml_tensor * c_attn_proj_b;

    // mlp
    struct ggml_tensor * c_mlp_fc_w;
    struct ggml_tensor * c_mlp_fc_b;

    struct ggml_tensor * c_mlp_proj_w;
    struct ggml_tensor * c_mlp_proj_b;
};

struct gpt_model {
    gpt_hparams hparams;

    // normalization
    struct ggml_tensor * ln_f_g;
    struct ggml_tensor * ln_f_b;

    struct ggml_tensor * wte;     // position embedding
    struct ggml_tensor * wpe;     //    token embedding
    struct ggml_tensor * lm_head; // language model head

    std::vector<gpt_layer> layers;

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

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

// load the model's weights from a file
bool gpt_model_load(const std::string & fname, gpt_model & model, gpt_vocab & vocab) {
    printf("%s: loading model from '%s'\n", __func__, fname.c_str());

    auto fin = std::ifstream(fname, std::ios::binary);
    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 != GGML_FILE_MAGIC) {
            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
            return false;
        }
    }

    // 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_head,  sizeof(hparams.n_head));
        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
        fin.read((char *) &hparams.ftype,   sizeof(hparams.ftype));

        const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;

        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
        printf("%s: ftype   = %d\n", __func__, hparams.ftype);
        printf("%s: qntvr   = %d\n", __func__, qntvr);

        hparams.ftype %= GGML_QNT_VERSION_FACTOR;
    }

    // load vocab
    {
        int32_t n_vocab = 0;
        fin.read((char *) &n_vocab, sizeof(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;
        std::vector<char> buf(128);

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

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

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

    // 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_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
    if (wtype == GGML_TYPE_COUNT) {
        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
                __func__, fname.c_str(), model.hparams.ftype);
        return false;
    }

    auto & ctx = model.ctx_w;

    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 += ggml_row_size(GGML_TYPE_F32, n_embd); // ln_f_g
        ctx_size += ggml_row_size(GGML_TYPE_F32, n_embd); // ln_f_b

        ctx_size += ggml_row_size(wtype,         n_vocab*n_embd); // wte
        ctx_size += ggml_row_size(GGML_TYPE_F32,   n_ctx*n_embd); // wpe
        ctx_size += ggml_row_size(wtype,         n_vocab*n_embd); // lm_head

        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_1_g
        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_1_b

        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_2_g
        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_2_b

        ctx_size += n_layer*(ggml_row_size(wtype,         3*n_embd*n_embd)); // c_attn_attn_w
        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, 3*n_embd));        // c_attn_attn_b

        ctx_size += n_layer*(ggml_row_size(wtype,         n_embd*n_embd));   // c_attn_proj_w
        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd));          // c_attn_proj_b

        ctx_size += n_layer*(ggml_row_size(wtype,         4*n_embd*n_embd)); // c_mlp_fc_w
        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, 4*n_embd));        // c_mlp_fc_b

        ctx_size += n_layer*(ggml_row_size(wtype,         4*n_embd*n_embd)); // c_mlp_proj_w
        ctx_size += n_layer*(ggml_row_size(GGML_TYPE_F32, 4*n_embd));        // c_mlp_proj_b

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

        ctx_size += (6 + 12*n_layer)*512; // object overhead

        printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
        printf("%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,
            /*.no_alloc   =*/ false,
        };

        model.ctx_w = ggml_init(params);
        if (!model.ctx_w) {
            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.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

        model.wte     = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
        model.wpe     = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
        model.lm_head = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);

        // map by name
        model.tensors["model/ln_f/g"] = model.ln_f_g;
        model.tensors["model/ln_f/b"] = model.ln_f_b;

        model.tensors["model/wte"]     = model.wte;
        model.tensors["model/wpe"]     = model.wpe;
        model.tensors["model/lm_head"] = model.lm_head;

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

            layer.ln_1_g        = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.ln_1_b        = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            layer.ln_2_g        = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
            layer.ln_2_b        = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype,           n_embd, 3*n_embd);
            layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);

            layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
            layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            layer.c_mlp_fc_w    = ggml_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd);
            layer.c_mlp_fc_b    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);

            layer.c_mlp_proj_w  = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
            layer.c_mlp_proj_b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);

            // map by name
            model.tensors["model/h" + std::to_string(i) + "/ln_1/g"]        = layer.ln_1_g;
            model.tensors["model/h" + std::to_string(i) + "/ln_1/b"]        = layer.ln_1_b;

            model.tensors["model/h" + std::to_string(i) + "/ln_2/g"]        = layer.ln_2_g;
            model.tensors["model/h" + std::to_string(i) + "/ln_2/b"]        = layer.ln_2_b;

            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;

            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;

            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"]    = layer.c_mlp_fc_w;
            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"]    = layer.c_mlp_fc_b;

            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"]  = layer.c_mlp_proj_w;
            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
        }
    }

    // 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);

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

    // load weights
    {
        size_t total_size = 0;

        bool has_lm_head = false;

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

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

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

            auto tensor = model.tensors[name];
            if (ggml_nelements(tensor) != nelements) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.c_str());
                return false;
            }

            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.c_str(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
                return false;
            }

            // for debugging
            if (0) {
                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.c_str(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
            }

            const size_t bpe = ggml_type_size(ggml_type(ttype));

            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.c_str(), ggml_nbytes(tensor), nelements*bpe);
                return false;
            }

            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));

            // GPT-2 models share the WTE tensor as the LM head
            if (name == "model/wte" && has_lm_head == false) {
                memcpy(model.lm_head->data, tensor->data, ggml_nbytes(tensor));
            }

            if (name == "model/lm_head") {
                has_lm_head = true;
            }

            total_size += ggml_nbytes(tensor);
        }

        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
    }

    fin.close();

    return true;
}

void gpt_split_words(std::string str, std::vector<std::string>& words) {
    const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
    const std::regex re(pattern);
    std::smatch m;

    while (std::regex_search(str, m, re)) {
        for (auto x : m) {
            words.push_back(x);
        }
        str = m.suffix();
    }
}

std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
    std::vector<std::string> words;

    // first split the text into words
    {
        std::string str = text;

        // Generate the subpattern from the special_tokens vector if it's not empty
        if (!vocab.special_tokens.empty()) {
            const std::regex escape(R"([\[\\\^\$\.\|\?\*\+\(\)\{\}])");
            std::string special_tokens_subpattern;
            for (const auto & token : vocab.special_tokens) {
                if (!special_tokens_subpattern.empty()) {
                    special_tokens_subpattern += "|";
                }
                special_tokens_subpattern += std::regex_replace(token, escape, R"(\$&)");
            }

            std::regex re(special_tokens_subpattern);
            std::smatch m;
            // Split the text by special tokens.
            while (std::regex_search(str, m, re)) {
                // Split the substrings in-between special tokens into words.
                gpt_split_words(m.prefix(), words);
                // Add matched special tokens as words.
                for (auto x : m) {
                    words.push_back(x);
                }
                str = m.suffix();
            }
            // Remaining text without special tokens will be handled below.
        }

        gpt_split_words(str, words);
    }

    // find the longest token that forms each word in words:
    std::vector<gpt_vocab::id> tokens;
    for (const auto & word : words) {
        for (int i = 0; i < (int) word.size(); ){
            for (int j = word.size() - 1; j >= i; j--){
                auto cand = word.substr(i, j-i+1);
                auto it = vocab.token_to_id.find(cand);
                if (it != vocab.token_to_id.end()){ // word.substr(i, j-i+1) in vocab
                    tokens.push_back(it->second);
                    i = j + 1;
                    break;
                }
                else if (j == i){ // word.substr(i, 1) has no matching
                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
                    i++;
                }
            }
        }
    }

    return tokens;
}

static std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string& input, char delimiter) {
    std::vector<gpt_vocab::id> output;
    std::stringstream ss(input);
    std::string token;

    while (std::getline(ss, token, delimiter)) {
        output.push_back(std::stoi(token));
    }

    return output;
}

static std::map<std::string, std::vector<gpt_vocab::id>> extract_tests_from_file(const std::string & fpath_test){
    if (fpath_test.empty()){
        fprintf(stderr, "%s : No test file found.\n", __func__);
        return std::map<std::string, std::vector<gpt_vocab::id>>();
    }

    std::map<std::string, std::vector<gpt_vocab::id>> tests;

    auto fin = std::ifstream(fpath_test, std::ios_base::in);
    const char * delimeter = " => ";
    const char del_tok = ',';
    std::string line;
    while (std::getline(fin, line)) {
        size_t delimiterPos = line.find(delimeter);
        if (delimiterPos != std::string::npos) {
            std::string text = line.substr(0, delimiterPos);
            std::string s_tokens = line.substr(delimiterPos + std::strlen(delimeter));
            tests[text] = parse_tokens_from_string(s_tokens, del_tok);
        }
    }
    return tests;
}

void test_gpt_tokenizer(gpt_vocab & vocab, const std::string & fpath_test){
    std::map<std::string, std::vector<gpt_vocab::id>> tests = extract_tests_from_file(fpath_test);

    size_t n_fails = 0;

    for (const auto & test : tests) {
        std::vector<gpt_vocab::id> tokens = gpt_tokenize(vocab, test.first);

        if (tokens != test.second){
            n_fails++;

            // print out failure cases
            fprintf(stderr, "%s : failed test: '%s'\n", __func__, test.first.c_str());
            fprintf(stderr, "%s : tokens in hf:   ", __func__);
            for (const auto & t : test.second) {
                fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
            }
            fprintf(stderr, "\n");
            fprintf(stderr, "%s : tokens in ggml: ", __func__);
            for (const auto & t : tokens) {
                fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
            }
            fprintf(stderr, "\n");
        }
    }

    fprintf(stderr, "%s : %zu tests failed out of %zu tests.\n", __func__, n_fails, tests.size());
}

gpt_vocab::id gpt_sample_top_k_top_p(
        const gpt_vocab & vocab,
        const float * logits,
        int    top_k,
        double top_p,
        double temp,
        std::mt19937 & rng) {
    int n_logits = vocab.id_to_token.size();

    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
    logits_id.reserve(n_logits);

    {
        const double scale = 1.0/temp;
        for (int i = 0; i < n_logits; ++i) {
            logits_id.push_back(std::make_pair(logits[i]*scale, i));
        }
    }

    // find the top K tokens
    std::partial_sort(
            logits_id.begin(),
            logits_id.begin() + top_k, logits_id.end(),
            [](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
        return a.first > b.first;
    });

    logits_id.resize(top_k);

    double maxl = -INFINITY;
    for (const auto & kv : logits_id) {
        maxl = std::max(maxl, kv.first);
    }

    // compute probs for the top K tokens
    std::vector<double> probs;
    probs.reserve(logits_id.size());

    double sum = 0.0;
    for (const auto & kv : logits_id) {
        double p = exp(kv.first - maxl);
        probs.push_back(p);
        sum += p;
    }

    // normalize the probs
    for (auto & p : probs) {
        p /= sum;
    }

    if (top_p < 1.0f) {
        double cumsum = 0.0f;
        for (int i = 0; i < top_k; i++) {
            cumsum += probs[i];
            if (cumsum >= top_p) {
                top_k = i + 1;
                probs.resize(top_k);
                logits_id.resize(top_k);
                break;
            }
        }

        cumsum = 1.0/cumsum;
        for (int i = 0; i < (int) probs.size(); i++) {
            probs[i] *= cumsum;
        }
    }

    //printf("\n");
    //for (int i = 0; i < (int) probs.size(); i++) {
    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
    //}
    //exit(0);

    std::discrete_distribution<> dist(probs.begin(), probs.end());
    int idx = dist(rng);

    return logits_id[idx].second;
}

// 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
//
bool gpt_eval(
        const gpt_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;

    static size_t buf_size = 256u*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
        //printf("\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,
        /*.no_alloc   =*/ false,
    };

    struct ggml_context * ctx0 = ggml_init(params);
    struct ggml_cgraph * gf = ggml_new_graph(ctx0);

    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 * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
    for (int i = 0; i < N; ++i) {
        ((int32_t *) position->data)[i] = n_past + i;
    }

    // wte + wpe
    struct ggml_tensor * inpL =
        ggml_add(ctx0,
                ggml_get_rows(ctx0, model.wte, embd),
                ggml_get_rows(ctx0, model.wpe, position));

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

        // norm
        {
            // [ 768, N]
            cur = ggml_norm(ctx0, inpL, hparams.eps);

            // cur = ln_1_g*cur + ln_1_b
            // [ 768, N]
            cur = ggml_add(ctx0,
                    ggml_mul(ctx0,
                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
                        cur),
                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
        }

        // attn
        // [2304, 768] - model.layers[il].c_attn_attn_w
        // [2304,   1] - model.layers[il].c_attn_attn_b
        // [ 768,   N] - cur (in)
        // [2304,   N] - cur (out)
        //
        // cur = attn_w*cur + attn_b
        // [2304, N]
        {
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].c_attn_attn_w,
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
                    cur);
        }

        // self-attention
        {
            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);

            // 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)
            // [64, N, 12]
            struct ggml_tensor * Q =
                ggml_permute(ctx0,
                        ggml_cpy(ctx0,
                            Qcur,
                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
                        0, 2, 1, 3);

            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
            // [64, n_past + N, 12]
            struct ggml_tensor * K =
                ggml_permute(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),
                        0, 2, 1, 3);

            // GG: flash attention
            //struct ggml_tensor * V =
            //    ggml_cpy(ctx0,
            //            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),
            //            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));

            //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);

            // K * Q
            // [n_past + N, N, 12]
            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);

            // KQ_scaled = KQ / sqrt(n_embd/n_head)
            // [n_past + N, N, 12]
            struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, 1.0f/sqrt(float(n_embd)/n_head));

            // KQ_masked = mask_past(KQ_scaled)
            // [n_past + N, N, 12]
            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);

            // KQ = soft_max(KQ_masked)
            // [n_past + N, N, 12]
            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);

            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
            // [n_past + N, 64, 12]
            struct ggml_tensor * V_trans =
                ggml_cpy(ctx0,
                        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),
                        ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd/n_head, n_head));

            // KQV = transpose(V) * KQ_soft_max
            // [64, N, 12]
            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);

            // KQV_merged = KQV.permute(0, 2, 1, 3)
            // [64, 12, N]
            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

            // cur = KQV_merged.contiguous().view(n_embd, N)
            // [768, N]
            cur = ggml_cpy(ctx0,
                    KQV_merged,
                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
        }

        // projection
        // [ 768, 768] - model.layers[il].c_attn_proj_w
        // [ 768,   1] - model.layers[il].c_attn_proj_b
        // [ 768,   N] - cur (in)
        // [ 768,   N] - cur (out)
        //
        // cur = proj_w*cur + proj_b
        // [768, N]
        {
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].c_attn_proj_w,
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
                    cur);
        }

        // add the input
        cur = ggml_add(ctx0, cur, inpL);

        struct ggml_tensor * inpFF = cur;

        // feed-forward network
        {
            // norm
            {
                cur = ggml_norm(ctx0, inpFF, hparams.eps);

                // cur = ln_2_g*cur + ln_2_b
                // [ 768, N]
                cur = ggml_add(ctx0,
                        ggml_mul(ctx0,
                            ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
                            cur),
                        ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
            }

            // fully connected
            // [3072, 768] - model.layers[il].c_mlp_fc_w
            // [3072,   1] - model.layers[il].c_mlp_fc_b
            // [ 768,   N] - cur (in)
            // [3072,   N] - cur (out)
            //
            // cur = fc_w*cur + fc_b
            // [3072, N]
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].c_mlp_fc_w,
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
                    cur);

            // GELU activation
            // [3072, N]
            cur = ggml_gelu(ctx0, cur);

            // projection
            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
            // [ 768,    1] - model.layers[il].c_mlp_proj_b
            // [3072,    N] - cur (in)
            // [ 768,    N] - cur (out)
            //
            // cur = proj_w*cur + proj_b
            // [768, N]
            cur = ggml_mul_mat(ctx0,
                    model.layers[il].c_mlp_proj_w,
                    cur);

            cur = ggml_add(ctx0,
                    ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
                    cur);
        }

        // input for next layer
        inpL = ggml_add(ctx0, cur, inpFF);
    }

    // norm
    {
        // [ 768, N]
        inpL = ggml_norm(ctx0, inpL, hparams.eps);

        // inpL = ln_f_g*inpL + ln_f_b
        // [ 768, N]
        inpL = ggml_add(ctx0,
                ggml_mul(ctx0,
                    ggml_repeat(ctx0, model.ln_f_g, inpL),
                    inpL),
                ggml_repeat(ctx0, model.ln_f_b, inpL));
    }

    // inpL = WTE * inpL
    // [ 768, 50257] - model.lm_head
    // [ 768, N]     - inpL
    inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);

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

    // run the computation
    ggml_build_forward_expand(gf, inpL);
    ggml_graph_compute_with_ctx(ctx0, gf, n_threads);

    //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 just for 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;
    }
    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}

void gpt_print_usage(int argc, char ** argv, const gpt_hparams & params) {
    fprintf(stderr, "usage: %s [options]\n", argv[0]);
    fprintf(stderr, "\n");
    fprintf(stderr, "options:\n");
    fprintf(stderr, "  -h, --help            show this help message and exit\n");
    fprintf(stderr, "  -s SEED, --seed SEED  RNG seed (default: -1)\n");
    fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
    fprintf(stderr, "  -p PROMPT, --prompt PROMPT\n");
    fprintf(stderr, "                        prompt to start generation with (default: random)\n");
    fprintf(stderr, "  -f FNAME, --file FNAME\n");
    fprintf(stderr, "                        load prompt from a file\n");
    fprintf(stderr, "  -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
    fprintf(stderr, "                        test tokenization\n");
    fprintf(stderr, "  -n N, --n_predict N   number of tokens to predict (default: %d)\n", params.n_predict);
    fprintf(stderr, "  --top_k N             top-k sampling (default: %d)\n", params.top_k);
    fprintf(stderr, "  --top_p N             top-p sampling (default: %.1f)\n", params.top_p);
    fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
    fprintf(stderr, "  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n);
    fprintf(stderr, "  --repeat-penalty N    penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty);
    fprintf(stderr, "  -b N, --batch_size N  batch size for prompt processing (default: %d)\n", params.n_batch);
    fprintf(stderr, "  -c N, --context N     context / KV cache size (default: %d)\n", params.n_ctx);
    fprintf(stderr, "  --ignore-eos          ignore EOS token during generation\n");
    fprintf(stderr, "  -ngl N, --gpu-layers N  number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers);
    fprintf(stderr, "  -m FNAME, --model FNAME\n");
    fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
    fprintf(stderr, "\n");
}

// Function to check if the next argument exists
static std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_hparams& params) {
    if (i + 1 < argc && argv[i + 1][0] != '-') {
        return argv[++i];
    } else {
        fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
        gpt_print_usage(argc, argv, params);
        exit(0);
    }
}

bool gpt_params_parse(int argc, char ** argv, gpt_hparams & params) {
    for (int i = 1; i < argc; i++) {
        std::string arg = argv[i];

        if (arg == "-s" || arg == "--seed") {
            params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "-t" || arg == "--threads") {
            params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "-p" || arg == "--prompt") {
            params.prompt = get_next_arg(i, argc, argv, arg, params);
        } else if (arg == "-n" || arg == "--n_predict") {
            params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "-np" || arg == "--n_parallel") {
            params.n_parallel = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "--top_k") {
            params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "--top_p") {
            params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "--temp") {
            params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "--repeat-last-n") {
            params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "--repeat-penalty") {
            params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "-b" || arg == "--batch_size") {
            params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "-c" || arg == "--context") {
            params.n_ctx= std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
            params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "--ignore-eos") {
            params.ignore_eos = true;
        } else if (arg == "-m" || arg == "--model") {
            params.model = get_next_arg(i, argc, argv, arg, params);
        } else if (arg == "-i" || arg == "--interactive") {
            params.interactive = true;
        } else if (arg == "-ip" || arg == "--interactive-port") {
            params.interactive = true;
            params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params));
        } else if (arg == "-h" || arg == "--help") {
            gpt_print_usage(argc, argv, params);
            exit(0);
        } else if (arg == "-f" || arg == "--file") {
            get_next_arg(i, argc, argv, arg, params);
            std::ifstream file(argv[i]);
            if (!file) {
                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
                break;
            }
            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
            if (params.prompt.back() == '\n') {
                params.prompt.pop_back();
            }
        } else if (arg == "-tt" || arg == "--token_test") {
            params.token_test = get_next_arg(i, argc, argv, arg, params);
        }
        else {
            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
            gpt_print_usage(argc, argv, params);
            exit(0);
        }
    }

    return true;
}

std::string gpt_random_prompt(std::mt19937 & rng) {
    const int r = rng() % 10;
    switch (r) {
        case 0: return "So";
        case 1: return "Once upon a time";
        case 2: return "When";
        case 3: return "The";
        case 4: return "After";
        case 5: return "If";
        case 6: return "import";
        case 7: return "He";
        case 8: return "She";
        case 9: return "They";
    }

    return "The";
}

int main(int argc, char ** argv) {
    ggml_time_init();

    const int64_t t_main_start_us = ggml_time_us();

    gpt_hparams params;

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

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

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

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

    int64_t t_load_us = 0;

    gpt_vocab vocab;
    gpt_model model;

    // load the model
    {
        const int64_t t_start_us = ggml_time_us();

        if (!gpt_model_load(params.model, model, vocab)) {
            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;

        test_gpt_tokenizer(vocab, params.token_test);
    }

    while(true) {
        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 = ::gpt_tokenize(vocab, params.prompt);

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

        printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
        printf("%s: number of tokens in prompt = %zu, first 8 tokens: ", __func__, embd_inp.size());
        for (int i = 0; i < std::min(8, (int) embd_inp.size()); i++) {
            printf("%d ", embd_inp[i]);
        }
        printf("\n\n");

        // submit the input prompt token-by-token
        // this reduces the memory usage during inference, at the cost of a bit of speed at the beginning
        std::vector<gpt_vocab::id> embd;

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

        for (size_t i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
            // predict
            if (embd.size() > 0) {
                const int64_t t_start_us = ggml_time_us();

                if (!gpt_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
                    printf("Failed to predict\n");
                    return 1;
                }

                t_predict_us += ggml_time_us() - t_start_us;
            }

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

            if (i >= embd_inp.size()) {
                // sample next token
                const int   top_k = params.top_k;
                const float top_p = params.top_p;
                const float temp  = params.temp;

                const int n_vocab = model.hparams.n_vocab;

                gpt_vocab::id id = 0;

                {
                    const int64_t t_start_sample_us = ggml_time_us();

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

                    t_sample_us += ggml_time_us() - t_start_sample_us;
                }

                // add it to the context
                embd.push_back(id);
            } else {
                // if here, it means we are still processing the input prompt
                for (size_t k = i; k < embd_inp.size(); k++) {
                    embd.push_back(embd_inp[k]);
                    if (int32_t(embd.size()) >= params.n_batch) {
                        break;
                    }
                }
                i += embd.size() - 1;
            }

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

            // end of text token
            if (embd.back() == 50256) {
                // report timing
                {
                    const int64_t t_main_end_us = ggml_time_us();

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

    ggml_free(model.ctx_w);

    return 0;
}