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e9192b0135
llama.cpp/gptneox-common.cpp
klosax e9192b0135
add gptneox gguf example
2023-07-30 15:05:37 +02:00

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#include "gptneox-common.h"
#include <cmath>
#include <cstring>
#include <fstream>
#include <regex>
#include <locale>
#include <codecvt>
#include <sstream>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
// Function to check if the next argument exists
std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& 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_params & 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 == "-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 == "-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 == "--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 == "-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;
}
void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & 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, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers);
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, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, "\n");
}
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";
default: return "To";
}
return "The";
}
std::string trim(const std::string & s) {
std::regex e("^\\s+|\\s+$");
return std::regex_replace(s, e, "");
}
std::string replace(const std::string & s, const std::string & from, const std::string & to) {
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;
}
void gpt_vocab::add_special_token(const std::string & token) {
special_tokens.push_back(token);
}
std::map<std::string, int32_t> json_parse(const std::string & fname) {
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;
}
std::string convert_to_utf8(const std::wstring & input) {
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
return converter.to_bytes(input);
}
std::wstring convert_to_wstring(const std::string & input) {
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
return converter.from_bytes(input);
}
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;
}
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;
}
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());
}
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
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;
}
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;
}
gpt_vocab::id gpt_sample_top_k_top_p_repeat(
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;
}
}
// printf("\n");
// for (int i = 0; i < (int) probs.size(); i++) {
// for (int i = 0; i < 10; i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
// }
std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);
return logits_id[idx].second;
}
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