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ggml : sync latest ggml lib
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@ggerganov
ggerganov committed Jun 25, 2023
1 parent 7dfc118 commit 5feb0df
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Showing 11 changed files with 7,316 additions and 1,526 deletions.
11 changes: 11 additions & 0 deletions examples/common-ggml.cpp
View file
Expand Up @@ -52,6 +52,11 @@ bool ggml_common_quantize_0(
case GGML_FTYPE_ALL_F32:
case GGML_FTYPE_MOSTLY_F16:
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16:
case GGML_FTYPE_MOSTLY_Q2_K:
case GGML_FTYPE_MOSTLY_Q3_K:
case GGML_FTYPE_MOSTLY_Q4_K:
case GGML_FTYPE_MOSTLY_Q5_K:
case GGML_FTYPE_MOSTLY_Q6_K:
{
fprintf(stderr, "%s: invalid model type %d\n", __func__, ftype);
return false;
Expand Down Expand Up @@ -187,6 +192,12 @@ bool ggml_common_quantize_0(
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_Q8_1:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
case GGML_TYPE_Q8_K:
case GGML_TYPE_COUNT:
{
fprintf(stderr, "%s: unsupported quantization type %d (%s)\n", __func__, ttype, ggml_type_name((ggml_type) ttype));
Expand Down
286 changes: 246 additions & 40 deletions examples/common.cpp
View file
Expand Up @@ -6,13 +6,21 @@
#include "dr_wav.h"

#include <cmath>
#include <cstring>
#include <fstream>
#include <regex>
#include <locale>
#include <codecvt>
#include <sstream>

#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif

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

bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
for (int i = 1; i < argc; i++) {
std::string arg = argv[i];
Expand Down Expand Up @@ -52,7 +60,10 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
if (params.prompt.back() == '\n') {
params.prompt.pop_back();
}
} else {
} else if (arg == "-tt" || arg == "--token_test") {
params.token_test = argv[++i];
}
else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
gpt_print_usage(argc, argv, params);
exit(0);
Expand All @@ -73,6 +84,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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);
Expand Down Expand Up @@ -117,6 +130,10 @@ std::string replace(const std::string & s, const std::string & from, const std::
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;

Expand Down Expand Up @@ -208,8 +225,28 @@ std::map<std::string, int32_t> json_parse(const std::string & fname) {
return result;
}

void gpt_vocab::add_special_token(const std::string & token) {
special_tokens.push_back(token);
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) {
Expand All @@ -218,70 +255,123 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
// first split the text into words
{
std::string str = text;
std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";

// 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 += token;
special_tokens_subpattern += std::regex_replace(token, escape, R"(\$&)");
}

// Modify the regex pattern with the generated special tokens subpattern
pat = special_tokens_subpattern + "|" + pat;
}

std::regex re(pat);
std::smatch m;

while (std::regex_search(str, m, re)) {
for (auto x : m) {
words.push_back(x);
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();
}
str = m.suffix();
// Remaining text without special tokens will be handled below.
}

gpt_split_words(str, words);
}

// find the longest tokens that form the words:
// find the longest token that forms each word in words:
std::vector<gpt_vocab::id> tokens;
for (const auto & word : words) {
if (word.size() == 0) continue;

int i = 0;
int n = word.size();
while (i < n) {
int j = n;
while (j > i) {
auto it = vocab.token_to_id.find(word.substr(i, j-i));
if (it != vocab.token_to_id.end()) {
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;
j = n;
i = j + 1;
break;
}
--j;
}
if (i == n) {
break;
}
if (j == i) {
auto sub = word.substr(i, 1);
if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
tokens.push_back(vocab.token_to_id.at(sub));
} else {
fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
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++;
}
++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());

Expand Down Expand Up @@ -381,6 +471,122 @@ gpt_vocab::id gpt_sample_top_k_top_p(
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;

}

bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector<std::vector<float>>& pcmf32s, bool stereo) {
drwav wav;
std::vector<uint8_t> wav_data; // used for pipe input from stdin
Expand Down

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