How to implement Infinite Context and Attention?

[calebnwokocha]

Please anyone who understand the method in this paper https://arxiv.org/abs/2404.07143 should help me implement the method in my code below for GPT-2 774M model. I think I am close to full implementation, but just a few things I am missing.

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#include "ggml.h"

#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include

#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_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        = 1024; // 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;

mutable std::vector<ggml_tensor*> past_keys;   // Stores past key tensors
mutable std::vector<ggml_tensor*> past_values; // Stores past value 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::vectorstd::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::vectorstd::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;

}

struct ggml_tensor* load_compressed_memory(
struct ggml_context* ctx,
int layer,
const gpt_model & model,
int n_past,
int n_embd) {

// Function to load compressed memory for the past keys and values at a given layer

// Memory compression parameters (can be adjusted based on the implementation specifics)
const int memory_compression_rank = 64; // Use a low-rank approximation to reduce memory size
const int memory_compression_kernel_size = 256; // Size of the kernelized memory

// Retrieve the cached keys and values from the previous layers (could be from a memory store or dynamic array)
// In this case, we'll assume that we're retrieving them for the specified layer.

// These keys and values are compressed representations of the past context
struct ggml_tensor* past_keys = model.past_keys[layer];
struct ggml_tensor* past_values = model.past_values[layer];

// Now we apply compression using low-rank approximation or kernel projection


// Initialize compressed keys and values
struct ggml_tensor* compressed_keys = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, memory_compression_kernel_size);
struct ggml_tensor* compressed_values = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, memory_compression_kernel_size);

// Here we use a simple projection for compression (could use SVD, kernel approximation, etc.)
// This will depend on how you want to reduce the dimensionality of the past context
// For example, a random projection could be used for kernelized compression.

// Apply a random projection for compression (simple demonstration, replace with your method)
struct ggml_tensor* projection_matrix = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, memory_compression_rank);
struct ggml_tensor* compressed_keys_proj = ggml_mul_mat(ctx, past_keys, projection_matrix);
struct ggml_tensor* compressed_values_proj = ggml_mul_mat(ctx, past_values, projection_matrix);

// Optionally, you can add or concatenate the compressed projections to the existing compressed keys/values
compressed_keys = ggml_add(ctx, compressed_keys, compressed_keys_proj);
compressed_values = ggml_add(ctx, compressed_values, compressed_values_proj);

// Normalize and finalize the compression (optional)
compressed_keys = ggml_norm(ctx, compressed_keys, 1e-6);
compressed_values = ggml_norm(ctx, compressed_values, 1e-6);

// Return the compressed keys and values as part of the attention mechanism
struct ggml_tensor* compressed_memory = ggml_concat(ctx, compressed_keys, compressed_values, 1024);
return compressed_memory;

}

// 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 & 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% for overhead
    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;
}

// Input embedding + positional encoding
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 and attention layers
    {
        cur = ggml_norm(ctx0, inpL, hparams.eps);
        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));
    }

    // Multi-head Infini-attention
    {
        struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0 * sizeof(float) * n_embd);

        // Load past memory for compression and update
        
                     std::cout << "Hello @";


        model.past_keys[il] = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_head);
        model.past_values[il] = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_head);


        struct ggml_tensor * Kcompressed = load_compressed_memory(ctx0, il, model, n_past, n_embd);
        struct ggml_tensor * Vcompressed = load_compressed_memory(ctx0, il, model, n_past, n_embd);

        ggml_tensor* new_tensor_K = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_head);
        ggml_tensor* new_tensor_V = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_head);

        struct ggml_tensor * K = ggml_concat(ctx0, Kcompressed, ggml_cpy(ctx0, Qcur, new_tensor_K), n_embd);
        struct ggml_tensor * V = ggml_concat(ctx0, Vcompressed, ggml_cpy(ctx0, Qcur, new_tensor_V), n_embd);

        struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Qcur);
        KQ = ggml_scale_inplace(ctx0, KQ, 1.0f / sqrt(float(n_embd) / n_head));
        KQ = ggml_soft_max_inplace(ctx0, KQ);

        struct ggml_tensor * output = ggml_mul_mat(ctx0, V, KQ);
        inpL = output;
    }
}

// Final norm and projection
inpL = ggml_norm(ctx0, inpL, hparams.eps);
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 = ggml_mul_mat(ctx0, model.lm_head, inpL);

ggml_build_forward_expand(gf, inpL);
ggml_graph_compute_with_ctx(ctx0, gf, n_threads);

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;
}

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";

}

//// Class for Infinite Context Handling
//std::vector tokens; // Store tokens dynamically
//
//// Add new tokens to context
//void add_tokens(const std::vector& new_tokens) {
// tokens.insert(tokens.end(), new_tokens.begin(), new_tokens.end());
//}
//
//// Retrieve relevant context
//std::vector get_context(int max_length) const {
// if (tokens.size() <= max_length) {
// return tokens;
// }
// return std::vector(tokens.end() - max_length, tokens.end());
//}

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;

}
`