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);

}

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;

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");

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";

std::string result = s;

size_t pos = 0;

while ((pos = result.find(from, pos)) != std::string::npos) {

result.replace(pos, from.length(), to);

pos += to.length();

}

return result;

std::map<std::string, int32_t> result;

// read file into string

std::string json;

{

std::ifstream ifs(fname);

if (!ifs) {

fprintf(stderr, "Failed to open %s\n", fname.c_str());

exit(1);

}

json = std::string((std::istreambuf_iterator<char>(ifs)),

(std::istreambuf_iterator<char>()));

}

if (json[0] != '{') {

return result;

}

// parse json

{

bool has_key = false;

bool in_token = false;

std::string str_key = "";

std::string str_val = "";

int n = json.size();

for (int i = 1; i < n; ++i) {

if (!in_token) {

if (json[i] == ' ') continue;

if (json[i] == '"') {

in_token = true;

continue;

}

} else {

if (json[i] == '\\' && i+1 < n) {

if (has_key == false) {

str_key += json[i];

} else {

str_val += json[i];

}

++i;

} else if (json[i] == '"') {

if (has_key == false) {

has_key = true;

++i;

while (json[i] == ' ') ++i;

++i; // :

while (json[i] == ' ') ++i;

if (json[i] != '\"') {

while (json[i] != ',' && json[i] != '}') {

str_val += json[i++];

}

has_key = false;

} else {

in_token = true;

continue;

}

} else {

has_key = false;

}

str_key = ::replace(str_key, "\\u0120", " " ); // \u0120 -> space

str_key = ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line

str_key = ::replace(str_key, "\\\"", "\""); // \\\" -> "

try {

result[str_key] = std::stoi(str_val);

} catch (...) {

//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());

}

str_key = "";

str_val = "";

in_token = false;

continue;

}

if (has_key == false) {

str_key += json[i];

} else {

str_val += json[i];

}

}

}

}

return result;

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<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;

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;

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;

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());

printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());

vocab.token_to_id = ::json_parse(fname);

for (const auto & kv : vocab.token_to_id) {

vocab.id_to_token[kv.second] = kv.first;

}

printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());

// print the vocabulary

//for (auto kv : vocab.token_to_id) {

// printf("'%s' -> %d\n", kv.first.data(), kv.second);

//}

return true;

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;

const gpt_vocab & vocab,

const float * logits,

const int32_t * last_n_tokens_data,

size_t last_n_tokens_data_size,

int top_k,

double top_p,

double temp,

int repeat_last_n,

float repeat_penalty,

std::mt19937 & rng) {

int n_logits = vocab.id_to_token.size();

const auto * plogits = logits;

const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);

if (temp <= 0) {

// select the token with the highest logit directly

float max_logit = plogits[0];

gpt_vocab::id max_id = 0;

for (int i = 1; i < n_logits; ++i) {

if (plogits[i] > max_logit) {

max_logit = plogits[i];

max_id = i;

}

}

return max_id;

}

std::vector<std::pair<double, gpt_vocab::id>> logits_id;

logits_id.reserve(n_logits);

{

const float scale = 1.0f/temp;

for (int i = 0; i < n_logits; ++i) {

// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)

// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main

if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {

// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability

if (plogits[i] < 0.0f) {

logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));

} else {

logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));

}

} else {

logits_id.push_back(std::make_pair(plogits[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;

}

}

std::discrete_distribution<> dist(probs.begin(), probs.end());

int idx = dist(rng);

return logits_id[idx].second;

const float rc = 1.0f / (2.0f * M_PI * cutoff);

const float dt = 1.0f / sample_rate;

const float alpha = dt / (rc + dt);

float y = data[0];

for (size_t i = 1; i < data.size(); i++) {

y = alpha * (y + data[i] - data[i - 1]);

data[i] = y;

}

const int n_samples = pcmf32.size();

const int n_samples_last = (sample_rate * last_ms) / 1000;

if (n_samples_last >= n_samples) {

// not enough samples - assume no speech

return false;

}

if (freq_thold > 0.0f) {

high_pass_filter(pcmf32, freq_thold, sample_rate);

}

float energy_all = 0.0f;

float energy_last = 0.0f;

for (int i = 0; i < n_samples; i++) {

energy_all += fabsf(pcmf32[i]);

if (i >= n_samples - n_samples_last) {

energy_last += fabsf(pcmf32[i]);

}

}

energy_all /= n_samples;

energy_last /= n_samples_last;

if (verbose) {

fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);

}

if (energy_last > vad_thold*energy_all) {

return false;

}

return true;

const size_t len0 = s0.size() + 1;

const size_t len1 = s1.size() + 1;

std::vector<int> col(len1, 0);

std::vector<int> prevCol(len1, 0);

for (size_t i = 0; i < len1; i++) {

prevCol[i] = i;

}

for (size_t i = 0; i < len0; i++) {

col[0] = i;

for (size_t j = 1; j < len1; j++) {

col[j] = std::min(std::min(1 + col[j - 1], 1 + prevCol[j]), prevCol[j - 1] + (i > 0 && s0[i - 1] == s1[j - 1] ? 0 : 1));

}

col.swap(prevCol);

}

const float dist = prevCol[len1 - 1];

return 1.0f - (dist / std::max(s0.size(), s1.size()));